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Innovation in Next Generation Sequencing

Making Genomics Available and Affordable for Everyone

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Evolving Health Testing and Prognosis

A cooperative-based clinical laboratory using state-of-the-art innovation in Next Generation Sequencing and machine/deep learning

NGS
NGS

NGS

Next Generation Sequencing

Genomic Sequencing Revolutionary Methods never used before. Next generation sequencing has changed medicine in the past decade in ways most people couldn’t imagine. It has allowed a rapid evolution to the field of precision medicine with new drugs with being approved regularly.

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Coop
Coop

Coop

We work together

A Cooperation Model of Bringing new solutions. Simply put a co-op means we all work together as a team to make things better. In the case of the Genomic Testing Cooperative it means that our lab works with other labs, physicians and patients to improve the communities access to cutting edge healthcare at an affordable price.

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AI Machine Learning
AI Machine Learning

AI Machine Learning

Bringing computer science to laboratories

Our AI helps us sort through curate large sets of data so we can quickly make sense of genomic alterations and assess their impact on a patient prognosis, diagnosis and help predict their response to therapy. We use machine learning to review these alterations and track their frequency allowing us to gain new insights about cancer.

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Committed to serve patients and Doctors quickly.

GTC is committed to serving patients with cancer quickly. We understand the urgency of the situation and work hard to deliver results quickly and accurately.

Less than 7 days TAT

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4.88 days

Hematology Profiles TAT

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6.33 days

Fusion Profiles TAT


(results March 2019)

A new way to internalize or outsource your genomic profiling for cancer

There are two ways to work together

a. Outsource Testing and b. Internalize Testing

First, you may be asking - What is a Co-op? A co-op is when people join together as a community to share resources By sharing resources, the costs of ownership is shared but you get all of the benefits of owning this equipment yourself. It’s there when you need it as much as you need it This allows for economies of scale to take place driving down the costs while keeping the quality high Being a member you also have a say with the other members of the co-op on the direction it takes and helps you get the products you need for your business You also have additional benefits that come along with it like deep expertise, a huge library of resources, an easy billing structure and more.

Learn more about the GTC co-op model

State-of-the-art Innovation

Machine/deep Learning meets Next Generation Sequencing

  • Who are we? Genomic Testing Cooperative (GTC) is creating a genomic testing lab combining next-gen sequencing (NGS) with AI that will disrupt the current model.
  • Why? Genomic testing is expensive and risky for smaller labs because competing with major NGS labs is challenging to get scale and payment.
  • How? Using a co-op model to reduce sales and service costs – the biggest impediment to profitability by sharing resources.
  • What makes it different? We use the latest advances in NGS combined with AI to compile the latest information to treat patients. The Co-op model produces big data for AI analysis and of value for Pharma and insurance companies.
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PRECISION
GENES
TESTS
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LOWER COST

OFFERING NEXT-GEN TESTS FOR ALL

Available and Affordable for Everyone

-Breaking News-

FDA Approves* Vitrakvi (larotrectinib), a targeted therapy for patients with solid tumors harboring an NTRK gene fusion, regardless of tumor type

Genomic Testing Cooperative (GTC) offers a comprehensive next-generation sequencing (NGS) Solid Tumor Fusion/ Expression profile specifically testing for fusions in NTRK1, NTRK2, and NTRK3 at an affordable price. 

Contact us today at (949) 540-9421 to order and learn more about our NGS offering. Get Results in 7 days.  

*Read about the approval  on Reuters

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Diagnosis and Classifications
Diagnosis and Classifications

Diagnosis and Classifications

Accurate variant calling using deep learning

Transforming diagnostic testing by adapting AI

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Monitoring Health
Monitoring Health

Monitoring Health

Innovative state-of-the-art new tests

Interpreting NGS data using deep learning

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Monitoring Therapy
Monitoring Therapy

Monitoring Therapy

Treatment recommendation aided by deep learning

Personalized medicine aided by AI

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Predicting Prognosis
Predicting Prognosis

Predicting Prognosis

Increasing efficiency using deep learning

Transforming diagnostic testing by adapting AI

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Selecting Therapy
Selecting Therapy

Selecting Therapy

Personalized medicine aided by deep learning

Interpreting NGS data using machine learning

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Advanced Genomic Testing

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OUR SCIENCE

FEATURED BLOG POSTS AND PUBLICATIONS

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Clonal Hematopoiesis

Clonal Hematopoiesis

Mutations in cell free DNA (cfDNA) or cells in the peripheral blood along with anemia or thrombocytopenia are the whole mark of myelodysplastic syndrome (MDS). The incidence of MDS is 3-4 per 100,000 per year but increases significantly above the age of 50 and it is at 30 per 100,000 per year in patients above the age of 70.  The diagnosis of MDS is confirmed when mutations in hematopoietic cells are detected at relatively high levels (>40% of cells).   However, mutations at low levels in few cells can be detected in normal individuals.   In general, random somatic mutations occur in normal cells, but rarely these cells evolve into viable clone, but with aging, the possibility of a clone to accumulate increases. Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence of low-level mutations in the peripheral blood in clinically normal individuals. CHIP is detected in 3-5% of normal individuals above the age of 50 and in approximately 10% of people aged 70 to 80. The most common mutation is on the DNMT3A gene, followed by TET2 and ASXL1. The rate of transformation to a hematological neoplasia is 0.5–1% per year. Clonal cytopenias of undetermined significance (CCUS) is defined by the presence of cytopenia (anemia, low platelets or white cells) along with low level mutations but does not meet World Health Organization (WHO)-defined criteria for MDS and the mutations are detected in <40% of cells. Approximately 25% to 65% of patients with cytopenia will have mutation in one or more genes.  These patients with mutations have significantly higher probability of developing MDS or other hematopoietic neoplasms (AML, MPN, lymphoma,…) within 5 years. In addition, recent studies linked mutations in peripheral blood to cardiovascular disease (CVD).  Recent data show that patients with CHIP have 4.0-times greater risk of myocardial infarction as compared to individuals without such clone.  The prevalence of CHIP in patients with coronary artery disease is reported to be at 18.2%.  In contrast, the prevalence of CHIP in centenarians is only at 2.5%.  It has been shown that mutations in TET2 gene, which is one of the commonly mutated genes in CHIP, are pro-inflammatory and lead to the development of atherosclerotic plaques.  Based on that it has been suggested that anti-inflammatory agents might slow the progression of cardiovascular disease in patients with low level mutations in peripheral blood. Therefore, testing the peripheral blood for the presence of mutations provides information for: Diagnosis of myelodysplastic syndrome (MDS). Detecting the presence of mutations in the presence of cytopenia and confirming the diagnosis of clonal cytopenias of undetermined (CCUS) Detecting the presence of CHIP to monitor the development of hematologic neoplasms, especially MDS Detecting CHIP and predicting increased risk of cardiovascular disease (CVD) Additional studies are needed to determine the clinical relevance of anti-inflammatory agents in reducing CVD or to determine the relationship between duration of the presence of CHIP or level of the mutated clone with progression of CVD or hematologic neoplasms. References: The shadowlands of MDS: idiopathic cytopenias of undetermined significance (ICUS) and clonal hematopoiesis of indeterminate potential (CHIP). Malcovati L, Cazzola M.  Hematology Am Soc Hematol Educ Program. 2015;2015:299-307. doi: 10.1182/asheducation-2015.1.299. PMID: Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Steensma DP, Bejar R, Jaiswal S, Lindsley RC, Sekeres MA, Hasserjian RP, Ebert BL.  2015 Jul 2;126(1):9-16. doi: 10.1182/blood-2015-03-631747.  PMID:  25931582. Deep Sequencing of Cell-Free Peripheral Blood DNA as a Reliable Method for Confirming the Diagnosis of Myelodysplastic Syndrome. Albitar F, Ma W, Diep K, De Dios I, Agersborg S, Thangavelu M, Brodie S, Albitar M. Genet Test Mol Biomarkers. 2016 Jul;20(7):341-5. doi: 10.1089/gtmb.2015.0278. Epub 2016 Jun 1. Age-related clonal hematopoiesis associated with adverse outcomes. Jaiswal S1, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, Higgins JM, Moltchanov V, Kuo FC, Kluk MJ, Henderson B, Kinnunen L, Koistinen HA, Ladenvall C, Getz G, Correa A, Banahan BF, Gabriel S, Kathiresan S, Stringham HM, McCarthy MI, Boehnke M, Tuomilehto J, Haiman C, Groop L, Atzmon G, Wilson JG, Neuberg D, Altshuler D, Ebert BL. N Engl J Med. 2014 Dec 25;371(26):2488-98. doi: 10.1056/NEJMoa1408617. Clonal Hematopoiesis and Its Impact on Cardiovascular Disease. Sano S, Wang Y, Walsh K. Circ J. 2018 Sep 4. doi: 10.1253/circj.CJ-18-0871. PMID: 30185689. Clinical Implications of Clonal Hematopoiesis. Steensma DP.  Mayo Clin Proc. 2018 Aug;93(8):1122-1130. doi: 10.1016/j.mayocp.2018.  PMID: 30078412. Myeloid cell contributions to cardiovascular health and disease. Nahrendorf M. Nat Med. 2018 Jun;24(6):711-720. doi: 10.1038/s41591-018-0064-0.   PMID: 29867229. Atherosclerosis and clonal hematopoyesis: A new risk factor. Páramo Fernández JA.  Clin Investig Arterioscler. 2018 May - Jun;30(3):133-136. doi: 10.1016/j.arteri.2018.03.001. Tet2-Mediated Clonal Hematopoiesis Accelerates Heart Failure Through a Mechanism Involving the IL-1β/NLRP3 Inflammasome. Sano S, Oshima K, Wang Y, MacLauchlan S, Katanasaka Y, Sano M, Zuriaga MA, Yoshiyama M, Goukassian D, Cooper MA, Fuster JJ, Walsh K.  J Am Coll Cardiol. 2018 Feb 27;71(8):875-886. doi: 10.1016/j.jacc.2017.12.037. Somatic Mutations and Clonal Hematopoiesis: Unexpected Potential New Drivers of Age-Related Cardiovascular Disease. Fuster JJ, Walsh K.  Circ Res. 2018 Feb 2;122(3):523-532. doi: 10.1161/CIRCRESAHA.117.312115. Clonal Hematopoiesis and Evolution to Hematopoietic Malignancies. Bowman RL, Busque L, Levine RL. Cell Stem Cell. 2018 Feb 1;22(2):157-170. doi: 10.1016/j.stem.2018.01.011. Risk and timing of cardiovascular death among patients with myelodysplastic syndromes. Brunner AM, Blonquist TM, Hobbs GS, Amrein PC, Neuberg DS, Steensma DP, Abel GA, Fathi AT.  Blood Adv. 2017 Oct 18;1(23):2032-2040. doi: 10.1182/bloodadvances.2017010165. eCollection 2017 Oct 24. The maturation of a 'neural-hematopoietic' inflammatory axis in cardiovascular disease. Stiekema LCA, Schnitzler JG, Nahrendorf M, Stroes ESG.  Curr Opin Lipidol. 2017 Dec;28(6):507-512. doi: 10.1097/MOL.0000000000000457. Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease. Jaiswal S, Natarajan P, Silver AJ, Gibson CJ, Bick AG, Shvartz E, McConkey M, Gupta N, Gabriel S, Ardissino D, Baber U, Mehran R, Fuster V, Danesh J, Frossard P, Saleheen D, Melander O, Sukhova GK, Neuberg D, Libby P, Kathiresan S, Ebert BL.  N Engl J Med. 2017 Jul 13;377(2):111-121. doi: 10.1056/NEJMoa1701719. Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice. Fuster JJ, MacLauchlan S, Zuriaga MA, Polackal MN, Ostriker AC, Chakraborty R, Wu CL, Sano S, Muralidharan S, Rius C, Vuong J, Jacob S, Muralidhar V, Robertson AA, Cooper MA, Andrés V, Hirschi KK, Martin KA, Walsh K.  2017 Feb 24;355(6327):842-847. doi: 10.1126/science.aag1381. Clonal hematopoiesis. Jan M, Ebert BL, Jaiswal S.  Semin Hematol. 2017 Jan;54(1):43-50. doi: 10.1053/j.seminhematol.2016.10.002. Epub 2016 Oct 20. Review. Clonal Hematopoiesis Associated With Adverse Outcomes After Autologous Stem-Cell Transplantation for Lymphoma. Gibson CJ, Lindsley RC, Tchekmedyian V, Mar BG, Shi J, Jaiswal S, Bosworth A, Francisco L, He J, Bansal A, Morgan EA, Lacasce AS, Freedman AS, Fisher DC, Jacobsen E, Armand P, Alyea EP, Koreth J, Ho V, Soiffer RJ, Antin JH, Ritz J, Nikiforow S, Forman SJ, Michor F, Neuberg D, Bhatia R, Bhatia S, Ebert BL.   J Clin Oncol. 2017 May 10;35(14):1598-1605. doi: 10.1200/JCO.2016.71.6712.

24. Jul 2016
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Chronic Lymphocytic Leukemia Prognostic Indicator

Chronic Lymphocytic Leukemia Prognostic Indicator

Circulating Ki-67 index in plasma as a biomarker and prognostic indicator in chronic lymphocytic leukemia. Ki-67 is a nuclear antigen that is expressed in all stages of the cell cycle, except G(0), and is widely used as a marker of cellular proliferation in human tumors. We recently showed that elevated levels of Ki-67 circulating in plasma (cKi-67) are associated with shorter survival in patients with acute lymphoblastic leukemia. The current study included 194 patients with CLL and 96 healthy control subjects. cKi-67 levels in plasma were determined using an electrochemiluminescent immunoassay. We normalized the cKi-67 level to the absolute number of lymphocytes in the patient's peripheral blood to establish the plasma cKi-67 index. The cKi-67 index showed significant correlation with lymph node involvement and Rai stage (P=0.05). Higher cKi-67 index values were significantly associated with shorter survival. Multivariate Cox proportional hazards regression analysis demonstrated that the association of the cKi-67 index with shorter survival was independent of IgV(H) mutation status. In a multivariate model incorporating the cKi-67 index with B2M and IgV(H), only cKi-67 index and B2M levels remained as independent predictors of survival. The results of this study suggest that the plasma cKi-67 index, along with B2M level, is a strong predictor of clinical behavior in CLL. Links to more resources Full Text Sources Elsevier Science ClinicalKey Europe PubMed Central - Author Manuscript PubMed Central - Author Manuscript Other Literature Sources COS Scholar Universe Medical Chronic Lymphocytic Leukemia - Genetic Alliance Miscellaneous NCI CPTAC Assay Portal    

20. Sep 2018
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Patients with Myeloproliferative Disorders

Patients with Myeloproliferative Disorders

MPL mutation profile in JAK2 mutation-negative patients with myeloproliferative disorders. Mutations in the thrombopoietin receptor gene (myeloproliferative leukemia, MPL) have been reported in patients with JAK2 V617F-negative chronic myeloproliferative disorders (MPDs). We evaluated the prevalence of MPL mutations relative to JAK2 mutations in patients with suspected MPDs. A total of 2790 patient samples submitted for JAK2 mutation analysis were tested using real-time polymerase chain reaction and bidirectional sequencing of plasma RNA. JAK2 V617F-negative samples were tested for JAK2 exons 12 to 14 mutations, and those with negative results were then tested for mutations in MPL exons 10 and 11. Of the 2790 patients, 529 (18.96%) had V617F, 12 (0.43%) had small insertions or deletions in exon 12, and 7 (0.25%) had other JAK2 mutations in exons 12 to 14. Of the 2242 JAK2 mutation-negative patients, 68 (3.03%) had MPL mutations. W515L was the predominant MPL mutation (n=46; 68%), and 10 (15%) patients had other W515 variants. The remaining MPL mutations (n=12, 17%) were detected at other locations in exons 10 and 11 and included 3 insertion/deletion mutations. The S505N mutation, associated with familial MPD, was detected in 3 patients. Overall, for every 100 V617F mutations in patients with suspected MPDs, there were 12.9 MPL mutations, 2.3 JAK2 exon 12 mutations, and 1.3 JAK2 exons 13 to 14 mutations. These findings suggest that MPL mutation screening should be performed before JAK2 exons 12 to 14 testing in JAK2 V617F-negative patients with suspected MPDs.    

21. Sep 2018
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Chimerism Analysis of Cell-Free DNA

Chimerism Analysis of Cell-Free DNA

Chimerism Analysis of Cell-Free DNA in Patients Treated with Hematopoietic Stem Cell Transplantation May Predict Early Relapse in Patients with Hematologic Malignancies. BACKGROUND: We studied DNA chimerism in cell-free DNA (cfDNA) in patients treated with HSCT. METHODS: Chimerism analysis was performed on CD3+ cells, polymorphonuclear (PMN) cells, and cfDNA using 16 small tandem repeat loci. The resulting labeled PCR-products were size-fractionated and quantified. RESULTS: Analyzing samples from 191 patients treated with HSCT for nonneoplastic hematologic disorders demonstrated that the cfDNA chimerism is comparable to that seen in PMN cells. Analyzing leukemia patients (N = 126) showed that, of 84 patients with 100% donor DNA in PMN, 16 (19%) had evidence of clinical relapse and >10% recipient DNA in the plasma. Additional 16 patients of the 84 (19%) showed >10% recipient DNA in plasma, but without evidence of relapse. Eight patients had mixed chimerism in granulocytes, lymphocytes, and plasma, but three of these patients had >10% recipient DNA in plasma compared to PMN cells and these three patients had clinical evidence of relapse. The remaining 34 patients showed 100% donor DNA in both PMN and lymphocytes, but cfDNA showed various levels of chimerism. Of these patients 14 (41%) showed laboratory or clinical evidence of relapse and all had >10% recipient DNA in cfDNA. CONCLUSION: Monitoring patients after HSCT using cfDNA might be more reliable than cellular DNA in predicting early relapse. Reference Image A: Copyright © 2016 Mahmoud Aljurf et al. Comparing percent of donor DNA in plasma cfDNA with polymorphonuclear (PMN) and lymphocytes. (a) cfDNA versus PMN DNA in patients with nonneoplastic diseases; (b) cfDNA versus Lymphocytes DNA in patients with nonneoplastic diseases; (c) cfDNA versus PMN in patients with hematologic neoplasms; (d) cfDNA versus lymphocyte DNA in patients with hematologic neoplasms. Reference Image B: Copyright © 2016 Mahmoud Aljurf et al. Chimerism pattern in a patient with CML treated with HSCT. Pretransplant and donor patterns are shown in the left panel. The middle and right panels show the patterns in PMN, lymphocytes, and plasma at two different points. The BCR-ABL fusion level as detected by RT/PCR is shown on top. The middle sample shows mixed chimerism in plasma only as well as residual BCR-ABL1 fusion RNA. The right panel shows the disappearance of the recipient DNA and fusion BCR-ABL1. Published Online: http://dx.doi.org/10.1155/2016/8589270 References E. D. Thomas, R. Storb, R. A. Clift et al., “Bone marrow transplantation,” The New England Journal of Medicine, vol. 292, no. 16, pp. 832–843, 1975. View at Publisher · View at Google Scholar · View at Scopus J. O. Armitage, “Bone marrow transplantation,” The New England Journal of Medicine, vol. 330, no. 12, pp. 827–838, 1994. View at Publisher · View at Google Scholar · View at Scopus R. Frederick and M. D. Appelbaum, “Hematopoietic-cell transplantation,” The New England Journal of Medicine, vol. 357, pp. 1472–1473, 2007. View at Publisher · View at Google Scholar S. Shenoy, T. Mohanakumar, G. Todd et al., “Immune reconstitution following allogeneic peripheral blood stem cell transplants,” Bone Marrow Transplantation, vol. 23, no. 4, pp. 335–346, 1999. View at Publisher · View at Google Scholar · View at Scopus N. Novitzky, G. M. Davison, G. Hale, and H. Waldmann, “Immune reconstitution at 6 months following T-cell depleted hematopoietic stem cell transplantation is predictive for treatment outcome,” Transplantation, vol. 74, no. 11, pp. 1551–1559, 2002. View at Publisher · View at Google Scholar · View at Scopus J. Storek, R. P. Witherspoon, and R. Storb, “T cell reconstitution after bone marrow transplantation into adult patients does not resemble T cell development in early life,” Bone Marrow Transplantation, vol. 16, no. 3, pp. 413–425, 1995. View at Google Scholar · View at Scopus W. Frankel, A. Chan, R. E. T. Corringham, S. Shepherd, A. Rearden, and J. Wang-Rodriguez, “Detection of chimerism and early engraftment after allogeneic peripheral blood stem cell or bone marrow transplantation by short tandem repeats,” American Journal of Hematology, vol. 52, no. 4, pp. 281–287, 1996. View at Publisher · View at Google Scholar · View at Scopus A. H. Elmaagacli, D. W. Beelen, H. W. Becks et al., “Molecular studies of chimerism and minimal residual disease after allogeneic peripheral blood progenitor cell or bone marrow transplantation,” Bone Marrow Transplantation, vol. 18, no. 2, pp. 397–403, 1996. View at Google Scholar · View at Scopus S.-J. Choi, K.-H. Lee, J.-H. Lee et al., “Prognostic value of hematopoietic chimerism in patients with acute leukemia after allogeneic bone marrow transplantation: a prospective study,” Bone Marrow Transplantation, vol. 26, no. 3, pp. 327–332, 2000. View at Publisher · View at Google Scholar · View at Scopus J. Serrano, J. Roman, J. Sanchez et al., “Molecular analysis of lineage-specific chimerism and minimal residual disease by RT-PCR of p210BCR-ABL and p190BCR-ABL after allogeneic bone marrow transplantation for chronic myeloid leukemia: increasing mixed myeloid chimerism and p190BCR-ABLdetection precede cytogenetic relapse,” Blood, vol. 95, no. 8, pp. 2659–2665, 2000. View at Google Scholar· View at Scopus A. H. Elmaagacli, K. Runkel, N. Steckel et al., “A comparison of chimerism and minimal residual disease between four different allogeneic transplantation methods in patients with chronic myelogenous leukemia in first chronic phase,” Bone Marrow Transplantation, vol. 27, no. 8, pp. 809–815, 2001. View at Publisher · View at Google Scholar · View at Scopus M. Lawler, P. Humphries, and S. R. McCann, “Evaluation of mixed chimerism by in vitro amplification of dinucleotide repeat sequences using the polymerase chain reaction,” Blood, vol. 77, no. 11, pp. 2504–2514, 1991. View at Google Scholar · View at Scopus I. Buño, P. Nava, A. Simón et al., “A comparison of fluorescent in situ hybridization and multiplex short tandem repeat polymerase chain reaction for quantifying chimerism after stem cell transplantation,” Haematologica, vol. 90, no. 10, pp. 1373–1379, 2005. View at Google Scholar · View at Scopus M. Adamek, G. Opelz, K. Klein, C. Morath, and T. H. Tran, “A fast and simple method for detecting and quantifying donor-derived cell-free DNA in sera of solid organ transplant recipients as a biomarker for graft function,” Clinical Chemistry and Laboratory Medicine, vol. 53, pp. 1434–6621, 2015. View at Publisher · View at Google Scholar J. Beck, M. Oellerich, U. Schulz et al., “Donor-derived cell-free DNA is a novel universal biomarker for allograft rejection in solid organ transplantation,” Transplantation Proceedings, vol. 47, no. 8, pp. 2400–2403, 2015. View at Publisher · View at Google Scholar R. Sachidanandam, D. Weissman, S. C. Schmidt et al., “A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms,” Nature, vol. 409, no. 6822, pp. 928–933, 2001.View at Publisher · View at Google Scholar · View at Scopus J. R. Gomez, J. Serrano, A. Jiménez et al., “Myeloid mixed chimerism is associated with relapse in bcr-abl positive patients after unmanipulated allogeneic bone marrow transplantation for chronic myelogenous leukemia,” Haematologica, vol. 85, no. 2, pp. 173–180, 2000. View at Google Scholar · View at Scopus P. Bader, J. Beck, A. Frey et al., “Serial and quantitative analysis of mixed hematopoietic chimerism by PCR in patients with acute leukemias allows the prediction of relapse after allogeneic BMT,” Bone Marrow Transplantation, vol. 21, no. 5, pp. 487–495, 1998. View at Publisher · View at Google Scholar ·View at Scopus  

22. Sep 2018
posted by admin
High-Sensitivity Sequencing for the Detection of Mutations

High-Sensitivity Sequencing for the Detection of Mutations

Using high-sensitivity sequencing for the detection of mutations in BTK and PLCγ2 genes in cellular and cell-free DNA and correlation with progression in patients treated with BTK inhibitors. Patients with chronic lymphocytic leukemia (CLL) that develop resistance to Bruton tyrosine kinase (BTK) inhibitors are typically positive for mutations in BTK or phospholipase c gamma 2 (PLCγ2). We developed a high sensitivity (HS) assay utilizing wild-type blocking polymerase chain reaction achieved via bridged and locked nucleic acids. We used this high sensitivity assay in combination with Sanger sequencing and next generation sequencing (NGS) and tested cellular DNA and cell-free DNA (cfDNA) from patients with CLL treated with the BTK inhibitor, ibrutinib. We also tested ibrutinib-naïve patients with CLL. HS testing achieved 100x greater sensitivity than Sanger. HS Sanger sequencing was capable of detecting < 1 mutant allele in background of 1000 wild-type alleles (1:1000). Similar sensitivity was achieved with HS NGS. No BTK or PLCγ2 mutations were detected in any of the 44 ibrutinib-naïve CLL patients. We demonstrate that without the HS testing 56% of positive samples would have been missed for BTK and 85% of PLCγ2 would have been missed. With the use of HS, we were able to detect multiple mutant clones in the same sample in 37.5% of patients; most would have been missed without HS testing. We also demonstrate that with HS sequencing, plasma cfDNA is more reliable than cellular DNA in detecting mutations. Our studies indicate that wild-type blocking and HS sequencing is necessary for proper and early detection of BTK or PLCγ2 mutations in monitoring patients treated with BTK inhibitors. Furthermore, cfDNA from plasma is very reliable sample-type for testing. Reference Image A: Increase in sensitivity with the addition of BNA/LNA oligonucleotides to sequencing (A) Sanger Sequencing. Genomic DNA positive for a BTK C481S (g>c) mutation is serially diluted with WT DNA to determine the limit of detection for the conventional and high-sensitivity (HS) Sanger based assays. Limit of detection is approximately 20% mutant allele in a background of WT by the conventional assay compared with 0.1% in the HS assay. (B) Next Generation Sequencing. The lower panel shows the high sensitivity testing of a sample which clearly demonstrates three different DNA strands with three different mutations: c.1442G>C (C481S), c.1441T>C (C481R) and c.1441T>A (C481S). The upper panel shows the same sample tested in conventional NGS without the high sensitivity approach. The three mutations were detectable but at a lower frequency. Reference Image B: Testing DNA derived from peripheral blood plasma is more sensitive than serum and even cells Temporally matched samples from the same patient (Patient # 4) reveals both a BTK C481R (t>c) and a PLCγ2 L845F (a>t) mutation in plasma. The BTK mutation is not detected in cells and both mutations are absent in serum.  

24. Sep 2018
posted by admin
Deep Sequencing

Deep Sequencing

Deep Sequencing of Cell-Free Peripheral Blood DNA as a Reliable Method for Confirming the Diagnosis of Myelodysplastic Syndrome. BACKGROUND: Demonstrating the presence of myelodysplastic syndrome (MDS)-specific molecular abnormalities can aid in diagnosis and patient management. We explored the potential of using peripheral blood (PB) cell-free DNA (cf-DNA) and next-generation sequencing (NGS). MATERIALS AND METHODS: We performed NGS on a panel of 14 target genes using total nucleic acid extracted from the plasma of 16 patients, all of whom had confirmed diagnoses for early MDS with blasts <5%. PB cellular DNA from the same patients was sequenced using conventional Sanger sequencing and NGS. RESULTS: Deep sequencing of the cf-DNA identified one or more mutated gene(s), confirming the diagnosis of MDS in all cases. Five samples (31%) showed abnormalities in cf-DNA by NGS that were not detected by Sanger sequencing on cellular PB DNA. NGS of PB cell DNA showed the same findings as those of cf-DNA in four of five patients but failed to show a mutation in the RUNX1 gene that was detected in one patient's cf-DNA. Mutant allele frequency was significantly higher in cf-DNA compared with cellular DNA (p = 0.008). CONCLUSION: These data suggest that cf-DNA, when analyzed using NGS, is a reliable approach for detecting molecular abnormalities in MDS and should be used to determine if bone marrow aspiration and biopsy are necessary.   Published Online:14 Jul 2016 https://doi.org/10.1089/gtmb.2015.0278 Links to more resources Medical Myelodysplastic syndromes - Genetic Alliance Myelodysplastic Syndromes - MedlinePlus Health Information Miscellaneous NCI CPTAC Assay Portal NCI CPTC Antibody Characterization Program

24. Sep 2018
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Press Release &#8211; Genomic Testing Cooperative

Press Release – Genomic Testing Cooperative

Genomic Testing Cooperative, a Limited Cooperative Association, Launches a New Platform for Affordable Genomic Cancer Profiling Irvine, California-November 1, 2018 - Genomic Testing Cooperative (GTC), a Limited Cooperative Association, announced today that it will offer multiple laboratory-developed genomic profiling tests for hematologic and solid tumors using next-generation sequencing (NGS) technology. The co-op provides a first of its kind platform for cooperation between commercial, hospital-based, and academic laboratories. This co-op structure allows the member laboratories to share resources and to participate in technological innovation and determining the type of tests being developed.  In addition, the co-op provides economies of scale that reduce costs of innovation in NGS. The co-op model is ideal for standardizing testing, obtaining FDA-clearance, and working with pharmaceutical companies on clinical trials. Complete molecular profiling including comprehensive interpretation, therapeutic options, and clinical trials are offered to co-op member laboratories at an affordable price. “This co-op platform aims to revolutionize the next-gen sequencing cancer testing approach.  Cooperation is critical for clinical laboratories to keep pace with the rapid advances in NGS technology, chemistry, bio-informatics, and clinical applications.” said founder Dr. Maher Albitar, the CEO and CMO at GTC.   “In the era of precision medicine, we at the Genomic Testing Cooperative, believe that every patient with cancer has the right to affordable molecular profiling of their cancer so clear treatment options can be explored.” At this time, 8 different cancer genomic profiling tests are available.   -Liquid biopsy for hematologic neoplasms is offered covering abnormalities in 177 genes.  Bone marrow biopsy, which is a very painful procedure, can potentially be avoided in more than 50% of patients if the liquid biopsy is used to profile molecular abnormalities in cell-free DNA. -The hematology profiling covers the coding sequence of 177 genes and provides complete information on diagnosis, prognosis, and heterogeneity in hematologic neoplasms.  Screening for fusion and translocation in hematologic neoplasms is also available covering fusions and expression profiles of 65 different genes. Complete DNA and RNA evaluation of hematologic neoplasms (GTC Hematology Profile PLUS) provide a comprehensive molecular evaluation of mutation, expression, and fusion/translocation of various types of hematologic neoplasms including diagnosis of Ph-like acute lymphoblastic leukemia and distinguishing between GCB and ABC diffuse large B-cell lymphoma. -The solid tumors molecular profile evaluates mutations and fusions in the entire coding sequence of 434 genes and provides information on Tumor Mutation Burden (TMB) and Microsatellite Instability (MSI).  A test for various fusions detected in solid tumors is also offered covering translocations involving 55 genes, including ALK, ROS1, RET, NTRK1, NTRK2, NTRK3 and sarcoma translocations. Generated molecular data is curated and checked for accuracy with the aid of proprietary software based on multiple algorithms and deep learning/machine learning approaches. About Genomic Testing Cooperative, LCA Genomic Testing Cooperative (GTC) is a privately-owned molecular testing company located in Irvine, CA.  The company operates based on a cooperative (co-op) business model. Members of the co-op hold type A shares with voting rights.  The company offers its patron members a full suite of comprehensive genomic profiling based mainly on next-generation sequencing. Molecular alterations are identified based on rigorous testing with the aid of specially developed algorithms to increase accuracy and efficiency.  The clinical relevance of the detected alterations is pulled from numerous databases using an internally developed software. Relevance of findings to diagnosis, prognosis, selecting therapy, and predicting outcome are reported to members. The co-op model allows GTC to make the testing and information platform available to members at a lower cost because of a lower overhead. GTC depends on the co-op members for marketing, sales, and billing.  For more information, please visit https://genomictestingcooperative.com/. Forward Looking Statements All of the statements, expectations, and assumptions contained in this press release are forward-looking statements. Such forward-looking statements are based on the GTC management’s current expectations.   A number of risks and uncertainties, including the ability of the company to recruit members for the co-op or to obtain adequate volume to reduce cost per test may differ from these expectations. Actual results and events may differ materially and adversely from these expectations. All information in this press release is as of the date of the release, and GTC undertakes no duty to update this information unless required by law. Company contact: Jennifer Varca Genomic Testing Cooperative. Jvarca@genomictestingcooperative.com

1. Nov 2018
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Acute Myeloid Leukemia | ASH Annual Meeting Abstract

Acute Myeloid Leukemia | ASH Annual Meeting Abstract

Session: 613. Acute Myeloid Leukemia: Clinical Studies: Poster I Molecular Epidemiologic Associations in Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) within the United States Dr. Albitar’s molecular work is presented at the ASH meeting in oral and poster presentations Saturday, December 1, 2018, 6:15 PM-8:15 PM | Hall GH (San Diego Convention Center) Background: Acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) are heterogeneous groups of disorders with a spectrum of clinical presentations and outcomes. Prognosis depends on various factors including age, karyotype, performance status, previous treatments, and mutation status. Genetic profiling with next generation sequencing (NGS) is increasingly being used at diagnosis to detect presence of somatic mutations for prognostic risk stratification, and identification of therapeutic targets. Here we seek to identify epidemiologic differences in genetic mutations based on population and demographic data in patients with a preliminary diagnosis of AML or MDS. Methods: NGS mutation data were collected for 62 genes related to AML/MDS on a total of 10,934 patient samples submitted for testing for suspected AML/MDS. Samples were run on either the 54-gene NeoType Myeloid Disorders Profile (Neogenomics) or the 37-gene OnkoSight Myeloid Malignancies Panel (Genpath). The frequency of gene mutations (i.e., the number of patient samples with mutations for each gene) was identified for 58 counties in the USA. Counties in which fewer than 50 patient samples were tested were excluded from the dataset to minimize sampling bias. The counties were then grouped into 3 categories ranging from most urban to most rural based on a modified version of the 2013 National Center for Health Statistics classification system G-1 – > 1,000,000 (N=34), G2 - 250,000 – 1,000,000 (N=16) and G3 < 250,000 (N=8). One-way ANOVA and subsequent T-tests were performed for all genes based on the 3 urban-rural groupings to determine if significant differences in frequency of mutations exist between the 3 groupings. Difference of proportions tests were performed to identify variations in the patterns of frequency between counties. Results: The top 10 most frequent mutations were TET2, ASXL1, DNMT3A, SRSF2, TP53, RUNX1, SF3B1, U2AF1, NRAS, and NPM1(highest to lowest). The three mutations with the widest range of variability across counties were DNMT3A, TET2, and ASXL1 (DTA mutations). The median age across all counties was 68 (range 44-77). The county with the youngest and highest median age was Montgomery, CA and Pinellas, FL, with ASXL1 and TET2 as the most frequent mutation, respectively. Sacramento, CA had an unusually high rate of ASXL1 mutations (> 24%). ASXL1 was also significantly higher than TET2 in Essex, NJ; Montgomery, MD; and Sacramento, CA. (p=0.0287, p < 0.0001, p < 0.0001, respectively). IDH1was higher than IDH2 for Martin, FL (p= 0.0481). The highest frequency of TP53 mutation (24%) was in Bexar, TX compared to Montgomery, MD which had the lowest (2%). When counties were grouped based on population density, the frequency of RUNX1 and SF3B1 were statistically different across the counties, highest in G2 compared to G1 and G3 (p= 0.0294 and 0.0010 respectively). Conclusion: Our retrospective observational study is the first of its kind to look at genetic mutations in AML/MDS patients across the United States using commercially available NGS platforms. In general, patients had analogous combinations and frequencies of mutations commonly seen in AML and MDS, and the wide variation in frequency of DTA mutations is consistent with information known about age-related clonal hematopoiesis. In counties that showed a higher rate of ASXL1 > TET2, there may be a potential environmental factor accounting for this difference as the reverse is more commonly seen. Likewise, IDH1 mutations are typically seen at a lower frequency compared to IDH2, and it is interesting to note the reverse in Martin, FL (G2) despite the mutation frequency of all other genes being similar compared to the median for all counties. Our data analysis also showed a significant difference in frequency of mutations for TP53, RUNX1, and SF3B1. These variations have important implications in regard to prognosis, and the approach to treatment. Our observations suggest further investigation is warranted to explore potential environmental exposures related to somatic mutational patterns in patients with AML and MDS. Read more about the poster on ASH site: 60th Annual Meeting and Exposition (December 1-4, 2018) Disclosures: McCloskey: Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Celgene Pharmaceuticals: Honoraria, Speakers Bureau; Amgen Pharmaceuticals: Speakers Bureau; Pfizer: Consultancy; Takeda Pharmaceuticals: Consultancy, Speakers Bureau; COTA: Equity Ownership. Koprivnikar: Amgen: Speakers Bureau; Otsuka: Consultancy; Alexion: Consultancy, Speakers Bureau. Program: Oral and Poster Abstracts Session: 613. Acute Myeloid Leukemia: Clinical Studies: Poster I Hematology Disease Topics & Pathways: AML, Adult, Diseases, Technology and Procedures, Study Population, Clinically relevant, Myeloid Malignancies, NGS Saturday, December 1, 2018, 6:15 PM-8:15 PM Hall GH (San Diego Convention Center) Adam Zrinski Albitar, BS1*, Neil Shah, MD2, James K McCloskey, MD3, Jamie L. Koprivnikar, MD4, Jianhua Zhao, PhD5*, Chirag Desai, MS, MBA5*, Sucha Sudarsanam, MS6*, Maher Albitar, MD7 and Catherine E. Lai, MD8 1Georgetown University School of Medicine, Washington, DC 2Georgetown University Hospital, Washington, DC 3John Theurer Cancer Center, Hackensack, NJ 4John Theurer Cancer Center at Hackensack University Medical Center, Hackensack Medical Center, NJ 5Bioreference Laboratory, Elmwood Park, NJ 6NeoGenomics, Inc., Aliso Viejo, CA 7 Former: NeoGenomics, Valley Center, CA - Now: Genomic Testing Cooperative, Irvine, CA 8Lombardi Comprehensive Cancer Center, Georgetown University Hospital, Washington, DC

26. Nov 2018
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Venetoclax in Combination with FLAG-IDA Chemotherapy (FLAG-V-I) for Fit | ASH ANNUAL MEETING ABSTRACT

Venetoclax in Combination with FLAG-IDA Chemotherapy (FLAG-V-I) for Fit | ASH ANNUAL MEETING ABSTRACT

Dr. Albitar’s molecular work is presented at the ASH meeting in oral and poster presentations Program: Oral and Poster Abstracts | Monday, December 3, 2018, 6:00 PM-8:00 PM Session: 616. Acute Myeloid Leukemia: Novel Therapy, excluding Transplantation: Poster III Background: Venetoclax (VEN) is a potent and selective small molecule BCL2 inhibitor, with activity both as a single agent in relapsed/refractory AML, and in frontline combinations with hypomethylating agents and low-dose cytarabine. The ability of VEN to reduce the apoptotic threshold indicates it may be effective in combination with genotoxic agents which induce apoptosis, such as the FLAG-IDA regimen. Objectives: A Phase 1b/2 clinical trial was designed to assess the safety and efficacy of VEN in combination with FLAG-IDA induction/consolidation. The primary safety endpoint was the overall incidence and severity of adverse events by CTCAE criteria. The primary efficacy endpoint was ORR by modified IWG AML criteria, and defined as CR, CRi, and PR. Secondary analyses include duration of response (DOR) and overall survival (OS), and exploratory analyses include gene expression signatures by RNA sequencing. Methods: Eligibility for the Phase 1b portion includes medically fit, relapsed/refractory (R/R) AML patients of any age with adequate organ function and ECOG PS ≤ 2. Patients receive FLAG-IDA induction/consolidation, in combination with VEN orally daily. FLAG-IDA induction for R/R AML consists of fludarabine 30 mg/m2 IV days 2-6, cytarabine 2 g/m2 IV days 2-6, idarubicin 6 mg/m2 IV days 4-6, and filgrastim 5 mcg/kg daily days 1-7 (or peg-filgrastim 6 mg after day 5 to replace remaining injections). The first cohort (dose -1) received FLAG-IDA with VEN 200 mg on days 1-21 of induction, incorporating a 2-day VEN dose ramp up. After the observation of gram-negative bacteremia and/or sepsis in 5 of the first 6 patients during cycle 1 nadir, an amended dose level -1 induction was designed with VEN 200 mg on days 1-14 and cytarabine 1.5 g/m². After completion of induction/consolidation, single-agent VEN at 400 mg orally continuously is provided as maintenance for patients not proceeding to SCT. The data cutoff for this analysis was 7.26.2018. Results: Twelve patients with a median age of 49 years (range 32 - 72) have been enrolled. All patients had relapsed/refractory AML with a median of 2 (range 1 - 4) prior treatments, and four (33%) patients had received ≥1 prior allogeneic SCT. Six patients (50%) had complex cytogenetics, 3 (25%) were intermediate risk, and 3 (25%) were favorable risk. Additional demographics including molecular annotation at study enrollment are provided in Table 1. The median number of FLAG-IDA + VEN cycles received is 2 (0.5 – 3). Severe adverse events regardless of causality were neutropenic fever/bacteremia (n=5), pneumonia/lung infection (n=4), sepsis (n=4), typhlitis (n=1), and hypotension (n=2). All cases of sepsis occurred in the original dose -1 cohort. No early mortality was observed on study (30-d and 60-d mortality 0%). Of 12 patients, one remains too early for response assessment. Of 11 patients, 8 patients (73%) achieved a best response of CR/CRi (7 CR, 1 CRi). Seven patients attained a best response within the first induction cycle, and one attained blast reduction after cycle 1 followed by CR after re-induction. Median time to ANC recovery > 500/ul in responding patients was 28 days (range 23 to 33 days). Of the 8 responding patients, three patients proceeded to allogeneic SCT, 2 remain on study, 2 patients relapsed, and 1 patient died in CR. Figure 1 depicts OS and DOR. With a median follow-up time of 4 months to date, median DOR has not been reached and the 6-month OS is 67%. NGS evaluation of RNA at pre-treatment and end of cycle 1 (EOC1) timepoints demonstrated no significant difference in BCL2 expression, either before/after therapy per patient, or based on clinical response. In patients who failed to achieve CR/CRi, significantly lower EOC1 expression (p=0.05) of BAX, BCLXL, BCL10, BCL2A1, BCL3, BCL9, TRS1, and TP53 was identified. Additionally, increased MCL1 expression at EOC1 was significantly (p=0.04) associated with relapse. Conclusion: FLAG-IDA chemotherapy with venetoclax demonstrates notable activity in a heavily pre-treated and R/R yet medically fit patient population. Neither prolonged cytopenias nor early mortality was observed. The ongoing Phase 1b portion aims to establish the best VEN dose in combination with intensive chemotherapy, to be followed with a Phase 2 portion with treatment arms for both newly diagnosed and R/R AML patients.   Courtney D. DiNardo, MD, MSc1, Maher Albitar, MD2*, Tapan M. Kadia, MD3, Kiran Naqvi, MD, MPH4, Kenneth Vaughan, RN4*, Antonio Cavazos, MSc5*, Sherry A. Pierce, BSN, BA4*, Koichi Takahashi, MD6, Steven M. Kornblau, MD7, Farhad Ravandi, MBBS8, Jorge E. Cortes, MD4, Hagop M. Kantarjian, MD9 and Marina Y. Konopleva, MD, PhD4 1Department of Leukemia, UT MD Anderson Cancer Center, Houston, TX 2Former: NeoGenomics, Valley Center, CA - Now: Genomic Testing Cooperative, Irvine, CA 3Department of Leukemia, M.D. Anderson Cancer Center, Houston, TX 4Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 5Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX 6Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, Houston, TX 7Department of Leukemia, MD Anderson Cancer Center, Houston, TX 8Department of Leukemia, University of Texas- MD Anderson Cancer Center, Houston, TX 9Department of Leukemia, University of Texas M.D. Anderson Cancer Center, Houston, TX

28. Nov 2018
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Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers | ASH ANNUAL MEETING ABSTRACT

Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers | ASH ANNUAL MEETING ABSTRACT

Dr. Albitar’s molecular work is presented at the ASH meeting in oral and poster presentations. Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers of Drug Resistance in CLL Patients Treated with Ibrutinib Program: Oral and Poster Abstracts | Monday, December 3, 2018: 11:45 AM Type: Oral Session: 642. CLL: Therapy, excluding Transplantation: Advances in CLL Using Novel Combination Therapy Introduction: We previously reported a prognostic scoring system in CLL using pre-treatment factors in patients treated with ibrutinib [Ahn et al, 2016 ASH Annual Meeting]. Here we present long-term follow-up results and validation of the prognostic models in a large independent cohort of patients. We also determine the incidence of resistance-conferring mutations in BTK and PLCG2 genes in different clinical risk groups. Methods and Patients: The discovery cohort comprised 84 CLL patients on a phase II study with either TP53 aberration (deletion 17p or TP53 mutation) or age ≥65 years (NCT01500733). The validation cohort comprised 607 patients pooled from four phase II and III studies for ibrutinib in treatment-naïve or relapsed/refractory CLL (NCT01105247; NCT01578707; NCT01722487; NCT01744691). All patients received single-agent ibrutinib 420mg once daily. We used Cox regression models to identify independent predictors of PFS, Kaplan-Meier method to estimate probabilities of PFS, log-rank test to compare PFS, and Cochran-Armitage trend test to compare the incidence of mutation among subgroups. We used R version 3.5.0 or SAS® version 9.3 for statistical analyses. For biomarker correlation, we tested cellular DNA or cell-free DNA collected from patients in the discovery cohort with the targeted sequencing of BTK and PLCG2 genes. Result: At a median follow-up of 5.2 years, 28 (33.3%) of 84 patients in the discovery cohort progressed or died. 52 (61.9%) patients had treatment-naïve CLL. Independent factors of PFS on univariate analysis were; TP53 aberration, prior treatment, and β-2 microglobulin (B2M) >4mg/L (P<0.05 for all tests). Unmutated IGHV and advanced Rai stage (III/IV) showed a trend toward inferior outcome without reaching statistical significance. Because higher levels of B2M were associated with relapsed/refractory CLL, we performed two multivariate Cox regression models to assess B2M and prior treatment status separately. Risk groups were determined by the presence of TP53 aberration, advanced Rai stage, and B2M >4mg/L for Model 1, and TP53 aberration, advanced Rai stage, and relapsed/refractory CLL for Model 2 (Table 1). The high-risk group had all three adverse risk factors; the intermediate-risk group had two risk factors; and the low-risk group, none or one. The median PFS of the high-risk group was 38.9 months for Model 1 and 38.4 months for Model 2, and was significantly shorter than those of intermediate and low-risk groups. In the validation cohort, 254 (41.8%) of 607 patients progressed or died at a median follow-up of 4.2 years. 167 (27.5%) patients had treatment-naïve CLL. Both models showed statistically significant differences in PFS by risk groups (Table 1). For the high-risk group, 4-year PFS was 30.2% in Model 1 and 30.5% in Model 2, which were inferior to those of intermediate (53.4 and 52.4%) and low-risk groups (68.7 and 73.7%). Model 1 classified 20% of patients and Model 2 classified 28% of patients to the high-risk group. BTK and PLCG2 mutations are common genetic drivers of ibrutinib resistance in CLL. To determine whether the incidence of these mutations correlates with prognostic risk groups, we performed targeted sequencing of BTK and PLCG2 of samples collected from patients in the discovery cohort. We used cell-free DNA for patients who received long-term ibrutinib (≥3 years) and had low circulating tumor burden, and cellular DNA, for samples collected within 3 years on ibrutinib or at progression. Of 84 patients, 69 (82.1%) were tested at least once, and 37 (44.0%) were tested at least twice. The frequency of testing was similar across the risk groups by two models (P>0.05). The cumulative incidences of mutations at 5 years in the low-, intermediate-, and high-risk groups were: 21.4%, 44.8% and 50%, respectively, by Model 1 (P=0.02); and 22.6%, 41.4% and 66.7%, respectively, by Model 2 (P=0.01). Conclusion: We developed and validated prognostic models to predict the risk of disease progression or death in CLL patients treated with ibrutinib. Risk groups classified by three commonly available pre-treatment factors showed statistically significant differences in PFS. The clinically-defined high-risk disease was linked to higher propensity to develop clonal evolution with BTK and/or PLCG2 mutations, which heralded ibrutinib resistance. Inhye E. Ahn, MD1, Xin Tian2*, Maher Albitar, MD3*, Sarah E. M. Herman, PhD4*, Erika M. Cook4*, Susan Soto4*, Wanlong Ma5*, David Ipe6*, L. Claire Tsao6*, Mei Cheng6*, James P. Dean6 and Adrian Wiestner, MD7 1Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, MD 2Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 3Former: NeoGenomics, Valley Center, CA - Now: Genomic Testing Cooperative, Irvine, CA 4Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 5NeoGenomics Laboratories, Irvine, CA 6Pharmacyclics LLC, an AbbVie Company, Sunnyvale, CA 7NIH, NHLBI, Bethesda, MD

29. Nov 2018
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Next-Gen Sequencing  made Available and Affordable 

Next-Gen Sequencing Revolutionary Methods never used before. NGS or Next-generation sequencing has changed medicine in the past decade in ways most people couldn’t imagine. It has allowed a rapid evolution to the field of precision medicine with new drugs with being approved regularly.

Evolving Health Testing and Prognosis

A cooperative-based clinical laboratory using state-of-the-art innovation in NGS Genomics and machine/deep learning.

We work together

A Cooperation Model of Bringing new solutions. Simply put a co-op means we all work together as a team to make things better. In the case of the Genomic Testing Cooperative, it means that our lab works with other labs, physicians and patients to improve the communities access to cutting-edge healthcare at an affordable price.

Artificial Intelligence Machine Learning Bringing computer science to laboratories

Our AI helps us sort through curate large sets of data so we can quickly make sense of genomic alterations and assess their impact on patient prognosis, diagnosis and help predict their response to therapy. We use machine learning to review these alterations and track their frequency allowing us to gain new insights into cancer. 

What is innovative about GTC 

  • Genomic Testing Cooperative (GTC) is creating a genomic testing lab combining next-gen sequencing (NGS) with AI that will disrupt the current model.
  • Genomic testing is expensive and risky for smaller labs because competing with major NGS labs is challenging to get scale and payment.
  • Using a co-op model to reduce sales and service costs – the biggest impediment to profitability by sharing resources.
  • We use the latest advances in NGS combined with AI to compile the latest information to treat patients.
  • The Co-op model produces big data for AI analysis and of value for Pharma and insurance companies.

Complete molecular profiling including comprehensive interpretation, therapeutic options, and clinical trials are offered to co-op member laboratories at an affordable price.

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