LBNL Tech Connect 2017 TC2017_flipbook | Page 6

Molecular Diagnostics for Precision Medicine Lawrence Berkeley National Laboratory Predicting a cancer patient’s response to radiation or chemotherapy informs treatment choices that improve quality of life and enable efficient allocation of medical and financial resources.   Following  are  representative  Berkeley  Lab  technologies  in  this  portfolio:     12-­‐Gene  Prognostic  Signature  for  Breast  Cancer  Survival   (2016-­‐181)   Breast  cancer  oncologists  lack  prognostic  markers  that  indicate  which  patients  will  benefit  from   adjuvant  therapy  and  which  patients  should  be  spared  its  toxic  effects.  This  12-­‐gene  signature   provides  a  way  to  predict  patient  prognosis  without  regard  to  conventional  factors  such  as   tumor  size,  histological  grade,  lymph  node  involvement,  etc.  The  signature  has  been  validated   using  a  multigene  co-­‐expression  network  analysis.     Gene  Signatures  that  Predict  Survival  Benefit  from  Radiotherapy  in  Luminal  A  and  Basal  Type   Breast  Cancer  Patients   (2017-­‐037)   The  relative  levels  of  seven  (7)  genes  for  Luminal  Type  A  breast  cancer  and  a  unique  set  of  17   genes  for  Basal  Type  breast  cancer  predict  survival  after  radiation  therapy  for  each  subtype.   The  gene  signatures  could  function  as  a  prognostic  test  applied  to  resected  tumor  tissue  and   could  enable  a  more  effective  assignment  of  patients  to  alternative  cancer  therapies,   minimizing  unnecessary  toxic  therapy  and  preserving  physical  resources  for  recovery.     Centromere  /  Kinetochore  Protein  Genes  for  Cancer  Prognosis,  Diagnosis,  and  Treatment   (2013-­‐163)   Unlike  cancer  diagnostics  and  therapies  that  measure  tumor  cell  growth,  this  Berkeley  Lab   technology  targets  the  centromere  and  kinetochore  pathway.  The  altered  expression  of  a   signature  of  21  Centromere  /  Kinetochore  (CEN/KT)  genes  and  proteins  is  linked  to  the   progression  of  breast,  stomach,  brain,  prostate,  lung  and  other  cancers  to  specific  clinical  stages   such  as  metastasis.  This  technology  has  the  potential  to  identify  patients  at  high  risk  for   aggressive  cancer  at  early  disease  stages,  predict  survival  rates  and  metastatic  relapse  for   patients  being  treated  for  cancer,  and  reduce  radiation  therapy  overtreatment  of  cancer   patients.  It  also  supports  the  development  of  new  targets  for  cancer  therapy.     27-­‐Gene  Prognostic  Signature  for  Lung  Cancer  Survival   (2017-­‐082)     Details  will  be  released  this  summer.     For  information  on  partnership  opportunities:   Peter  Bluford,  Technology  Commercialization  Associate   [email protected],  510-­‐486-­‐7954   ipo.lbl.gov