Foundation Medicine, Inc.

Ucl Business Plc

Gritstone Bio, Inc.

Genentech, Inc.

Gritstone Oncology, Inc.

Vanderbilt University

Roman Yelensky

Philip James Stephens

Doron Lipson

Garrett Michael Frampton

James Xin Sun

Brendan Bulik-Sullivan

Geoffrey Alan Otto

Matthew J Hawryluk

Alexander Nevin Parker

Sean R Downing

Maureen T Cronin

Jennifer Busby

Mikhail G Shapiro

Chris Hendrik Boshoff

Timothy Robert Fenton

Matthias Alexander Lechner

Vincent A Miller

David Fabrizio

Adnan Derti

Jeffrey S Ross

Priti Hegde

Gary Palmer

Mima Jarosz

Mirna Jarosz

Sanjeev Mariathasan

Jie He

Richard Bourgon

Gregg Fine

Thomas Francis Boucher

Joel Robert Greenbowe

Ciaran Daniel Scallan

Karin Jooss

Leonid Gitlin

Marcin Kowanetz

Amy Rachel Rappaport

David Shames

Douglas B Johnson

Wei Zou

Jeffrey Alan Sosman

Christine Marie Lovly

Alexander N Parker

Philip J Stephens

Monica Lane

Christine Denise Palmer

Caitlin F Connelly

Zachary R Chalmers

Garrett Michaell Frampton

Priti Hedge

Methods and systems for analyzing genetic variants are disclosed. The methods and systems can be used to classify a variant in a tumor sample as germline, somatic, subclonal, or ambiguous. The variant can be classified by fitting a genome-wide copy number model to a sequence coverage input and a SNP allele frequency input. The fitted model can be used to determine a tumor purity, and a total copy number and a minor allele copy number for each of a plurality of genomic segments. Using the tumor purity, the total copy number and the minor allele copy number for the genomic segment comprising the variant, and the variant allele frequency for the variant in the tumor sample, the variant can be classified as germline, somatic, subclonal or ambiguous.

Analysis of genetic variants

Novel fusion molecules and uses are disclosed.

Fusion molecules and uses thereof

The present invention provides therapeutic and diagnostic methods and compositions for cancer, for example, bladder cancer. The invention provides methods of treating bladder cancer, methods of determining whether a patient suffering from bladder cancer is likely to respond to treatment comprising a PD-L1 axis binding antagonist, methods of predicting responsiveness of a patient suffering from bladder cancer to treatment comprising a PD-L1 axis binding antagonist, and methods of selecting a therapy for a patient suffering from bladder cancer, based on somatic mutation levels of genes of the invention (e.g., somatic mutation levels in a tumor sample obtained from the patient).

Therapeutic and diagnostic methods for cancer

A method of analyzing a tumor sample comprising:

Optimization of multigene analysis of tumor samples

Novel mutant ESR1 molecules and uses are disclosed.

Estrogen receptor mutations and uses thereof

Given a set of therapeutic epitopes, a cassette sequence is designed to reduce the likelihood that junction epitopes are presented in the patient. The cassette sequence is designed by taking into account presentation of junction epitopes that span the junction between a pair of therapeutic epitopes in the cassette. The cassette sequence may be designed based on a set of distance metrics each associated with a junction of the cassette. The distance metric may specify a likelihood that one or more of the junction epitopes spanning between a pair of adjacent epitopes will be presented.

Reducing junction epitope presentation for neoantigens

Disclosed herein are compositions that include antigen-encoding nucleic acid sequences having multiple iterations of KRAS neoepitope-encoding sequences and/or lacking immunodominant epitopes. Also disclosed are nucleotides, cells, and methods associated with the compositions including their use as vaccines.

Multiepitope vaccine cassettes

Methods of treating a head and neck cancer are disclosed.

Human papilloma virus as predictor of cancer prognosis

Methods and compositions for treating cancer such as melanomas are disclosed.

Methods of treating cancer

The present invention provides therapeutic and diagnostic methods and compositions for cancer, for example, lung cancer (e.g., NSCLC), bladder cancer (e.g., UC), kidney cancer (e.g., RCC), breast cancer (e.g., TNBC), or melanoma. The invention provides methods of treating cancer (e.g., lung cancer (e.g., NSCLC), bladder cancer (e.g., UC), kidney cancer (e.g., RCC), breast cancer (e.g., TNBC), or melanoma), methods of determining whether a patient suffering from cancer (e.g., lung cancer (e.g., NSCLC), bladder cancer (e.g., UC), kidney cancer (e.g., RCC), breast cancer (e.g., TNBC), or melanoma) is likely to respond to treatment comprising a PD-L1 axis binding antagonist, methods of predicting responsiveness of a patient suffering from cancer (e.g., lung cancer (e.g., NSCLC), bladder cancer (e.g., UC), kidney cancer (e.g., RCC), breast cancer (e.g., TNBC), or melanoma) to treatment comprising a PD-L1 axis binding antagonist, and methods of selecting a therapy for a patient suffering from cancer (e.g., lung cancer (e.g., NSCLC), bladder cancer (e.g., UC), kidney cancer (e.g., RCC), breast cancer (e.g., TNBC), or melanoma), based on a tissue tumor mutational burden (tTMB) score, which reflects somatic mutation levels of genes in a tumor tissue sample obtained from the patient, alone or in combination with PD-L1 expression levels (e.g., PD-L1 expression levels in tumor or tumor-infiltrating immune cells in a tumor sample (tumor area) obtained from the patient).

A method for identifying neoantigens that are likely to be presented on a surface of tumor cells of a subject. Peptide sequences of tumor neoantigens are obtained by sequencing the tumor cells of the subject. The peptide sequence of each of the neoantigens is associated with one or more k-mer blocks of a plurality of k-mer blocks of the nucleotide sequencing data of the subject; The peptide sequences and the associated k-mer blocks are input into a machine-learned presentation model to generate presentation likelihoods for the tumor neoantigens, each presentation likelihood representing the likelihood that a neoantigen is presented by an MHC allele on the surfaces of the tumor cells of the subject. A subset of the neoantigens is selected based on the presentation likelihoods.

Neoantigen identification using hotspots

Growing community of inventors

Newton, MA, United States of America

Roman Yelensky