1) An analysis was conducted of malignancy data from clinical trials of tofacitinib, an oral Janus kinase inhibitor for rheumatoid arthritis (RA). Over 5,600 patients were treated with tofacitinib across phase 2, 3, and long-term extension studies.
2) 107 patients treated with tofacitinib developed malignancies excluding non-melanoma skin cancer (NMSC), with rates similar to those expected in RA patients and the general population. The most common malignancies were lung cancer, breast cancer, and lymphoma.
3) The incidence of malignancies was stable over time with tofacitinib exposure and was not higher than placebo or the active control ad
Co-Chairs Roy S. Herbst, MD, PhD, and Lecia V. Sequist, MD, MPH, prepared useful Practice Aids pertaining to EGFR-mutated lung cancer for this CME activity titled “New Milestones and Changing Standards of Care in EGFR-Mutated NSCLC: Expanding the Benefits of Genomic Testing and EGFR-Targeted Therapy to Early-Stage Lung Cancer.” For the full presentation, downloadable Practice Aids, and complete CME information, and to apply for credit, please visit us at http://bit.ly/36aVo39. CME credit will be available until March 8, 2022.
Co-Chairs Roy S. Herbst, MD, PhD, and Lecia V. Sequist, MD, MPH, prepared useful Practice Aids pertaining to EGFR-mutated lung cancer for this CME activity titled “New Milestones and Changing Standards of Care in EGFR-Mutated NSCLC: Expanding the Benefits of Genomic Testing and EGFR-Targeted Therapy to Early-Stage Lung Cancer.” For the full presentation, downloadable Practice Aids, and complete CME information, and to apply for credit, please visit us at http://bit.ly/36aVo39. CME credit will be available until March 8, 2022.
Roy H. Decker, MD, PhD; Kristin Higgins, MD; and Jyoti D. Patel, MD, prepared useful practice aids pertaining to immunotherapies in lung cancer for this CME/MOC activity titled “NSCLC Tumor Board: Navigating the Evolving Role of Immunotherapy in Multimodal Management of Locally Advanced and Early-Stage Lung Cancer.” For the full presentation, monograph, complete CME/MOC information, and to apply for credit, please visit us at http://bit.ly/2mFfEWE. CME/MOC credit will be available until October 22, 2020.
Can brain atrophy measurement help us in monitoring MS progression in routine...MS Trust
This presentation by Dana Horáková, Department of Neurology and Centre of Clinical Neuroscience at the Charles University in Prague, looks at why and how we should measure brain atrophy.
It was presented at the MS Trust Annual Conference in November 2014.
David R. Jones, MD, and Roy S. Herbst, MD, PhD, prepared useful practice aids pertaining to lung cancer for this CME activity titled "Turning Tides in Targeted Therapy for Early-Stage EGFR-Mutated NSCLC: Latest Data and Practical Guidance for Thoracic Surgeons and the Multidisciplinary Team on the Emerging Role of EGFR-Targeted Therapy in Resectable Lung Cancer." For the full presentation, complete CME information, and to apply for credit, please visit us at https://bit.ly/2PSVELG. CME credit will be available until November 9, 2021.
Jessica Donington, MD, Natasha Leighl, MD, MMSc, FRCPC, FASCO, and Brendon Stiles, MD, prepared useful practice aids pertaining to the role of immunotherapy in lung cancer for this CME/MOC/CNE activity titled, "The Expanding Role of Immunotherapy in Locally Advanced and Earlier Stages of Lung Cancer: Rationale, Current Evidence, Key Trials, and Implications for Thoracic Surgeons." For the full presentation, monograph, complete CME/MOC/CNE information, and to apply for credit, please visit us at http://bit.ly/2WibbtU. CME/MOC/CNE credit will be available until June 16, 2020.
Roy H. Decker, MD, PhD; Kristin Higgins, MD; and Jyoti D. Patel, MD, prepared useful practice aids pertaining to immunotherapies in lung cancer for this CME/MOC activity titled “NSCLC Tumor Board: Navigating the Evolving Role of Immunotherapy in Multimodal Management of Locally Advanced and Early-Stage Lung Cancer.” For the full presentation, monograph, complete CME/MOC information, and to apply for credit, please visit us at http://bit.ly/2mFfEWE. CME/MOC credit will be available until October 22, 2020.
Can brain atrophy measurement help us in monitoring MS progression in routine...MS Trust
This presentation by Dana Horáková, Department of Neurology and Centre of Clinical Neuroscience at the Charles University in Prague, looks at why and how we should measure brain atrophy.
It was presented at the MS Trust Annual Conference in November 2014.
David R. Jones, MD, and Roy S. Herbst, MD, PhD, prepared useful practice aids pertaining to lung cancer for this CME activity titled "Turning Tides in Targeted Therapy for Early-Stage EGFR-Mutated NSCLC: Latest Data and Practical Guidance for Thoracic Surgeons and the Multidisciplinary Team on the Emerging Role of EGFR-Targeted Therapy in Resectable Lung Cancer." For the full presentation, complete CME information, and to apply for credit, please visit us at https://bit.ly/2PSVELG. CME credit will be available until November 9, 2021.
Jessica Donington, MD, Natasha Leighl, MD, MMSc, FRCPC, FASCO, and Brendon Stiles, MD, prepared useful practice aids pertaining to the role of immunotherapy in lung cancer for this CME/MOC/CNE activity titled, "The Expanding Role of Immunotherapy in Locally Advanced and Earlier Stages of Lung Cancer: Rationale, Current Evidence, Key Trials, and Implications for Thoracic Surgeons." For the full presentation, monograph, complete CME/MOC/CNE information, and to apply for credit, please visit us at http://bit.ly/2WibbtU. CME/MOC/CNE credit will be available until June 16, 2020.
Dr. Feroze Momin presents Chronic Lymphocytic Leukemia - Review and new Insights.
To read about Dr. Feroze Momin: http://conquercancers.com/ourdoctorso1.html
To read about Cancer Treatment Center in Michigan:
http://conquercancers.com
Report Back from San Antonio Breast Cancer Symposium (SABCS 2022)bkling
Curious about the latest developments in Early-Stage Breast Cancer and Metastatic Breast Cancer Research? Join us as Dr. Anne Blaes, the Division Director of Hematology/Oncology/Transplantation and Professor in Hematology/Oncology at the University of Minnesota, breaks down the most recent developments released at the annual San Antonio Breast Cancer Symposium regarding early-stage and metastatic breast cancer research.
Edward B. Garon, MD, MS, Jamie E. Chaft, MD, and Matthew D. Hellmann, MD, prepared useful Practice Aids pertaining to lung cancer management for this CME/MOC/CE activity titled "Improving Patient Outcomes With Cancer Immunotherapies Throughout the Lung Cancer Continuum: State of the Science and Implications for Practice." For the full presentation, monograph, complete CME/MOC/CE information, and to apply for credit, please visit us at http://bit.ly/2ATq0qp. CME/MOC/CE credit will be available until November 21, 2019.
Ομιλία - Παρουσίαση: “Βιοδείκτες: Η Κλινική τους Αξία και η Σχέση τους με τον ΕΟΠΥΥ”
Νικόλαος Τσούλος, MSc, MBA, Βιοχημικός, Διευθύνων Σύμβουλος GeneKor Medical SA
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Cancer cell metabolism: special Reference to Lactate Pathway
Tofacitinib, an oral janus kinase inhibitor, analysis of malignancies across the ra clinical program
1. 1
Tofacitinib, an Oral Janus Kinase Inhibitor:
Analysis of Malignancies Across the
Rheumatoid Arthritis Clinical Program
JR Curtis
Presentation Number: 802
JR Curtis,1 X Mariette,2 EB Lee,3 B Benda,4 I Kaplan,5 K Soma,5 D Gruben,5
J Geier,6 L Wang,5 R Riese5
1University of Alabama at Birmingham, Birmingham, AL, USA; 2Paris-Sud University, Paris, France; 3Seoul National University,
Seoul, Republic of Korea; 4Pfizer Inc, Collegeville, PA, USA; 5Pfizer Inc, Groton, CT, USA; 6Pfizer Inc, New York, NY, USA
802
Disclosure
X Mariette and JR Curtis have received grant/research support from
Pfizer Inc, and have acted as consultants for Pfizer Inc
EB Lee has acted as a consultant for Pfizer Inc
B Benda, I Kaplan, K Soma, D Gruben, J Geier, L Wang and
R Riese are all employees and shareholders of Pfizer Inc
2
802
3
Introduction
Tofacitinib is a novel oral Janus kinase inhibitor for the treatment of
rheumatoid arthritis (RA)
Tofacitinib 5 mg and 10 mg twice daily demonstrated efficacy and a
manageable safety profile in patients with RA in randomized Phase 21–6
and Phase 37–12 studies
Certain types of malignancies may occur in higher frequency in patients
with RA
Additionally, malignancies are a concern with therapeutic agents that treat
RA by modulation of the immune system
Presented here is an analysis of malignancy data from the clinical
development program of tofacitinib in patients with moderate to severe
active RA. Data cut-off date: April 2013
1. Fleischmann R et al. Arthritis Rheum 2012; 64: 617–629. 2. Kremer JM et al. Arthritis Rheum 2009; 60: 1895–1905. 3. Kremer JM et al.
Arthritis Rheum 2012; 64: 970–981. 4. Tanaka Y et al. Arthritis Care Res 2011; 63: 1150–1158. 5. Tanaka Y et al. Arthritis and
Rheumatism 2011; 63: S854. 6. McInnes IB et al. Ann Rheum Dis 2013; 2013 Mar 12. [Epub ahead of print] 7. van Vollenhoven RF et al.
N Engl J Med 2012; 367: 508–519. 8. van der Heijde D et al. Arthritis Rheum 2013; 65: 559–570. 9. Kremer J et al. Ann Intern Med 2013;
159: 253–261. 10. Fleischmann R et al. N Engl J Med 2012; 367: 495–507. 11. Burmester G et al. Lancet 2013; 381: 451–460. 12. Lee EB
et al. Arthritis Rheum 2012; 64(10 Suppl): S1049, Abst 2486.
802
Methods
Data were pooled from 6 randomized Phase 21–6, 6 randomized Phase 37–12
and 213-14 open-label long-term extension (LTE) studies
Tofacitinib was dosed as either monotherapy or with nonbiologic
disease-modifying antirheumatic drugs
● Phase 2 studies were of 6 weeks’ to 6 months’ duration and Phase 3 studies were
of ≥6 months’ duration
● Two ongoing open-label LTE studies enrolled patients who participated in the prior
Phase 2 and 3 studies.
4
1. Fleischmann R et al. Arthritis Rheum 2012; 64: 617–629. 2. Kremer JM et al. Arthritis Rheum 2009; 60: 1895–1905. 3. Kremer JM et al. Arthritis Rheum
2012; 64: 970–981. 4. Tanaka Y et al. Arthritis Care Res 2011; 63: 1150–1158. 5. Tanaka Y et al. Arthritis and Rheumatism 2011; 63: S854. 6. McInnes IB
et al. Ann Rheum Dis 2013; 2013 Mar 12. [Epub ahead of print] 7. van Vollenhoven RF et al. N Engl J Med 2012; 367: 508–519. 8. van der Heijde D et al.
Arthritis Rheum 2013; 65: 559–570. 9. Kremer J et al. Ann Intern Med 2013; 159: 253–261. 10. Fleischmann R et al. N Engl J Med 2012; 367: 495–507.
11. BurmesterG et al. Lancet 2013; 381: 451–460. 12. Lee EB et al. Arthritis Rheum 2012; 64(10 Suppl): S1049, Abst 2486. 13. W ollenhaupt J et al.
Arthritis Rheum 2012; 64: S548. 14. Yamanaka et al. Arthritis Rheum 2011; 63: S473.
802
Analysis
Malignancies were identified by review of investigator-reported adverse
events (AEs), serious AE reports, and output from the central laboratory
histology review
A malignancy over-read process involved a centralized, external, blinded
review of each biopsy case performed by at least two independent, board-
certified pathologists
● Discordance in opinion was resolved by using the most conservative
interpretation from either the local or central pathology review
● Patients with no pathology report were classified according to the type of
malignancy reported by the study investigator
Standard incidence ratios (SIRs) compared with the Surveillance
Epidemiology and End Result (SEER) database (United States) were also
calculated for select malignancies
5
802
Patient demography in Phase 3 studiesa
6
aTofacitinib monotherapyor tofacitinib+ methotrexate (MTX) or other nonbiologic disease-modifying antirheumaticdrugs (DMARDs); bPatients
randomized to placebo were advanced to tofacitinib at Month 3 or Month 6; cActive control arm of adalimumab(ADA) or ADA + MTX. ADA was
dosed 40 mg subcutaneouslyevery 2 weeks
Tofacitinib
5 mg BID
Tofacitinib
10 mg BID
Placebo to
tofacitinib
5 mg BIDb
Placebo to
tofacitinib
10 mg BIDb
ADAc
n=1216 n=1214 n=343 n=338 n=204
Mean age (range),
years
53.2
(18-86)
52.5
(18-85)
52.8
(18-82)
52.2
(18-80)
52.5
(23-77)
Gender, % Female 84.5 84.9 81.0 81.4 79.4
Race, %
White 60.6 61.0 66.8 62.1 72.5
Black 3.7 2.9 2.3 4.7 1.5
Asian 26.9 25.9 23.6 25.1 14.2
Other 8.8 10.2 7.3 8.0 11.8
Disease duration, years 8.7 9.1 8.9 9.7 8.1
Phase 2 and LTE baseline characteristics were similar
802
2. 2
Exposure to tofacitinib
Number of patients receiving tofacitinib in Phase 2, Phase 3 and LTE studies
(all doses combined):
7
Duration of exposure (LTE studies only):
● Tofacitinib 5 mg BID up to 72 months; mean 554.0 days, maximum 2187 days
● Tofacitinib 10 mg BID up to 66 months; mean 997.2 days, maximum 1996 days
No. of
patients
Tofacitinib treatment
802
4748
3873
3080
1910
556
0
1000
2000
3000
4000
5000
> 6 months > 1 year > 2 years > 3 years > 4 years
Common types of malignancies
107 out of 5671 patients treated with tofacitinib were observed with malignancies
(excluding NMSC)
● 66 patients were observed with NMSC
8
Placebo treated patients (≤6 month duration):
● No malignancies (excluding NMSC)
● 2 out of 681 patients receiving placebo were observed with NMSC
Adalimumab treated patients:
● 1 renal cell carcinoma and 1 non-small-cell lung cancer
66
24
19
10
0
10
20
30
40
50
60
70
NMSC Lung Breast Lymphoma
No. of
patients
†
802
†all female patients; NMSC, non-melanomaskin cancer
Incidence rates for all malignancies
(excluding NMSC)
9
Rate/100
patient
years
of
observation
(95% CI)
†Phase 2, Phase 3 and LTE studies, all doses
ADA, adalimumab;CI, confidence interval; LTE; long-term extension; N, total numberof patients; n, number of patients with an event;
NMSC, non-melanoma skin cancer; PBO, placebo; pyo, patient years of observation
0
4
3
2
1
5671
107
12664
1609
13
1501
681
0
203
204
1
179
1452
41
4005
3375
42
5191
1587
8
1464
4827
83
9196
N
n
pyo
Phase 3 LTE (all tofacitinib)
Tofacitinib
All†
10 mg PBO ADA 5 mg 10 mg5 mg LTE All
0.85 0.90
0.55
0.87
1.02 0.81
0.00
0.56
802
Incidence rates over time
for all malignancies (excluding NMSC)
10
2.00
0.80
0.60
0.40
0.20
0.00
0-6
Rate/100
patient
years of
observation
(95% CI)
All malignancies (excluding NMSC)
6-12 12-18 18-24 24-30 30-36 36-42 >42
1.80
1.60
1.40
1.20
1.00
Time period (months)
802
CI, confidence interval; NMSC, non-melanomaskin cancer
Incidence rates for lung cancer
11
4
0
Tofacitinib
All†
Rate/100
patient
years
of
observation
(95% CI)
10 mg PBO ADA 5 mg 10 mg
3
2
1
5 mg LTE All
5671
24
1609
1
681
0
204
1
1452
5
3375
14
1587
3
4827
19
N
n
Phase 3 LTE (all tofacitinib)
0.19 0.210.20 0.07 0.13
0.27
0.56
0.00
†Phase 2, Phase 3 and LTE studies, all doses
ADA, adalimumab;CI, confidence interval; LTE, long-term extension; N, total numberof patients; n, number of patients with an event; PBO, placebo
802
Incidence rates for breast cancer‡
12
0.8
0.0
Tofacitinib
All†
Rate/100
patient
years
of
observation
(95% CI)
10 mg PBO ADA 5 mg 10 mg
0.6
0.4
0.2
5 mg LTE All
4712
19
1355
3
553
0
162
0
1452
7
3375
8
1310
1
4827
15
N
n
Phase 3 LTE (all tofacitinib)
0.000.00
0.18 0.16
0.08
0.24
0.18
0.15
‡all female patients; †Phase 2, Phase 3 and LTE studies, all doses
ADA, adalimumab;CI, confidence interval; LTE, long-term extension; N, total numberof patients; n, number of patients with an event; PBO, placebo
802
3. 3
Incidence rate for lymphoma
13
0.0
Tofacitinib
All†
Rate/100
patient
years
of
observation
(95% CI)
10 mg PBO ADA 5 mg 10 mg5 mg LTE All
5677
10
1609
3
681
0
204
0
1452
3
3375
2
1587
0
4827
5
N
n
Phase 3 LTE (all tofacitinib)
0.05
0.00
0.20
0.08
0.04
0.000.00
†Phase 2, Phase 3 and LTE studies, all doses; for completeness2 additionalcases were included from an ongoing blinded Phase 3 study
ADA, adalimumab;CI, confidence interval; LTE, long-term extension; N, total numberof patients; n, number of patients with an event; PBO, placebo
802
0.08
0.8
0.6
0.4
0.2
Incidence rates for non-melanoma
skin cancer (NMSC)
14
Tofacitinib
All†
Rate/100
patient
years
of
observation
(95% CI)
10 mg PBO ADA 5 mg 10 mg5 mg LTE All
5671
66
1609
8
681
2
204
2
1587
6
N
n
Phase 3 LTE (all tofacitinib)
1.130.99
0.53
0.35
0.41 0.53
0.84
0.62
8
0
6
4
2
†Phase 2, Phase 3 and LTE studies, all doses
ADA, adalimumab;CI, confidence interval; LTE, long-term extension; N, total numberof patients; n, number of patients with an event; PBO, placebo
802
1452
14
3375
43
4827
57
Incidence* rates
for tofacitinib-treated patients
15
*IRs from randomized clinical trial data; †SIRs from published observation date; $United States National Data Bank for Rheumatic
Diseases; #United States National Data Bank for Rheumatic Diseases and cohorts of RA patients treated with DMARDs; @SEER database
(US General Population), which excludes NMSC; ‡female patients
CI, confidence interval; bDMARDs, biologic disease modifying antirheumatic drugs; IR, incidence rate; N/A, not available; NMSC, non-
melanoma skin cancer; pyo, patient years of observation; RA, rheumatoid arthritis; SEER, Surveillance Epidemiology and End Result
database; SIR, standardized incidence ratio (as compared with the SEER); TNF, tumor necrosis factor
802
IR
Events/100 pyo
(95% CI)
Tofacitinib
N=4791
All malignancies
(excluding NMSC)
0.85
(0.70, 1.02)
Lung 0.190
(0.127, 0.283)
Breast‡ 0.18
(0.12, 0.28)
Lymphoma 0.08
(0.04, 0.14)
NMSC 0.53
(0.41, 0.67)
Incidence* and standardized† incidence rates
for tofacitinib-treated patients
16
*IRs from randomized clinical trial data; †SIRs from published observation date; $United States National Data Bank for Rheumatic
Diseases; #United States National Data Bank for Rheumatic Diseases and cohorts of RA patients treated with DMARDs; @SEER database
(US General Population), which excludes NMSC; ‡female patients
CI, confidence interval; bDMARDs, biologic disease modifying antirheumatic drugs; IR, incidence rate; N/A, not available; NMSC, non-
melanoma skin cancer; pyo, patient years of observation; RA, rheumatoid arthritis; SEER, Surveillance Epidemiology and End Result
database; SIR, standardized incidence ratio (as compared with the SEER); TNF, tumor necrosis factor
802
IR
Events/100 pyo
(95% CI)
Tofacitinib
N=4791
SIR
(95% CI)
Tofacitinib
N=4791
All malignancies
(excluding NMSC)
0.85
(0.70, 1.02)
SEER: -1.08
(0.89, 1.31)
Lung 0.190
(0.127, 0.283)
SEER: 1.91
(1.22, 2.84)
Breast‡ 0.18
(0.12, 0.28)
SEER: 0.77
(0.46, 1.20)
Lymphoma 0.08
(0.04, 0.14)
SEER: 2.58
(1.24, 4.74)
NMSC 0.53
(0.41, 0.67)
N/A
Incidence* and standardized† incidence rates
for tofacitinib- and bDMARD-treated patients
17
*IRs from randomized clinical trial data; †SIRs from published observation date; $United States National Data Bank for Rheumatic
Diseases; #United States National Data Bank for Rheumatic Diseases and cohorts of RA patients treated with DMARDs; @SEER database
(US General Population), which excludes NMSC; ‡female patients
CI, confidence interval; bDMARDs, biologic disease modifying antirheumatic drugs; IR, incidence rate; N/A, not available; NMSC, non-
melanoma skin cancer; pyo, patient years of observation; RA, rheumatoid arthritis; SEER, Surveillance Epidemiology and End Result
database; SIR, standardized incidence ratio (as compared with the SEER); TNF, tumor necrosis factor
802
IR
Events/100 pyo
(95% CI)
Tofacitinib
N=4791
SIR
(95% CI)
Tofacitinib
N=4791
IR
Events/100 pyo
(95% CI)
TNF inhibitors/
bDMARDs
N=4791
SIR
(95% CI)
TNF inhibitors/
bDMARDs
N=4791
All malignancies
(excluding NMSC)
0.85
(0.70, 1.02)
SEER: -1.08
(0.89, 1.31)
0.3-1.771-4 0.9-1.1@5
Lung 0.190
(0.127, 0.283)
SEER: 1.91
(1.22, 2.84)
0.23-0.26# 1.08-3.56
Breast‡ 0.18
(0.12, 0.28)
SEER: 0.77
(0.46, 1.20)
0.11-0.34# 0.4-1.686
Lymphoma 0.08
(0.04, 0.14)
SEER: 2.58
(1.24, 4.74)
0.06-0.14$1,2 1.1-9.72,7-14
NMSC 0.53
(0.41, 0.67)
N/A 0.23-1.3415,16 N/A
18
Conclusions
Overall, the malignancies that occurred in the tofacitinib
development program in RA are consistent with the type and
distribution of malignancies expected for this population of patients
with moderate to severe active RA
The IRs for all malignancies (excluding NMSC), lung cancer, breast
cancer, lymphoma and NMSC are consistent with published
estimates in RA patients treated with biologic and nonbiologic
DMARDS and do not show an increase over time
Longer-term follow-up is necessary to further evaluate the potential
risk of malignancies in the tofacitinib RA program
802
4. 4
Incidence* and standardized† incidence rates
for tofacitinib treated patients
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Acknowledgments
The authors would like to thank the patients, investigators and study
team who were involved in the studies (NCT00413660,
NCT00603512, NCT01059864, NCT00147498, NCT00550446,
NCT00687193, NCT00814307, NCT00847613, NCT00960440,
NCT00856544, NCT00853385, NCT00413699, NCT00661661)
This study was sponsored by Pfizer Inc
Editorial support was provided by Martin Goulding, PhD, of
Complete Medical Communications and was funded by Pfizer Inc
20
802
Questions