US 12,331,359 B2
Neoantigens and uses thereof for treating cancer
Marta Luksza, New York, NY (US); Vinod P. Balachandran, New York, NY (US); Arnold J. Levine, Princeton, NJ (US); Jedd D. Wolchok, New York, NY (US); Taha Merghoub, Jersey City, NJ (US); Steven D. Leach, New York, NY (US); Timothy A. Chan, New York, NY (US); Benjamin D. Greenbaum, New York, NY (US); and Michael Laessig, Cologne (DE)
Assigned to Michael Laessig, New York, NY (US); Icahn School of Medicine at Mount Sinai, New York, NY (US); The Simons Center for Systems Biology at the Institute for Advanced Study, Princeton, NJ (US); and Memorial Sloan Kettering Cancer Center, New York, NY (US)
Appl. No. 16/478,818
Filed by Icahn School of Medicine at Mount Sinai, New York, NY (US); The Simons Center for Systems Biology at the Institute for Advanced Study, Princeton, NJ (US); Memorial Sloan Kettering Cancer Center, New York, NY (US); and Michael Laessig, New York, NY (US)
PCT Filed Jan. 18, 2018, PCT No. PCT/US2018/014282
§ 371(c)(1), (2) Date Jul. 17, 2019,
PCT Pub. No. WO2018/136664, PCT Pub. Date Jul. 26, 2018.
Claims priority of provisional application 62/618,540, filed on Jan. 17, 2018.
Claims priority of provisional application 62/582,851, filed on Nov. 7, 2017.
Claims priority of provisional application 62/554,232, filed on Sep. 5, 2017.
Claims priority of provisional application 62/448,247, filed on Jan. 19, 2017.
Claims priority of provisional application 62/448,291, filed on Jan. 19, 2017.
Claims priority of provisional application 62/447,852, filed on Jan. 18, 2017.
Prior Publication US 2020/0232040 A1, Jul. 23, 2020
Int. Cl. G16B 30/10 (2019.01); A61K 35/15 (2015.01); A61K 35/17 (2015.01); A61K 39/00 (2006.01); C12Q 1/6886 (2018.01); G01N 33/569 (2006.01); G01N 33/574 (2006.01); G16B 20/10 (2019.01); G16B 20/20 (2019.01); G16B 20/30 (2019.01); G16B 30/20 (2019.01); G16B 50/00 (2019.01); G16B 30/00 (2019.01)
CPC C12Q 1/6886 (2013.01) [A61K 35/15 (2013.01); A61K 35/17 (2013.01); A61K 39/00 (2013.01); A61K 39/0011 (2013.01); A61K 39/00117 (2018.08); G01N 33/56977 (2013.01); G01N 33/57438 (2013.01); G16B 20/10 (2019.02); G16B 20/20 (2019.02); G16B 20/30 (2019.02); G16B 30/10 (2019.02); G16B 30/20 (2019.02); G16B 50/00 (2019.02); C12Q 2600/106 (2013.01); C12Q 2600/156 (2013.01); G01N 33/574 (2013.01); G01N 2800/52 (2013.01); G16B 30/00 (2019.02)] 21 Claims
OG exemplary drawing
 
1. A method for selecting a human subject afflicted with a cancer for treatment with a checkpoint blockade immunotherapy comprising:
(A) obtaining a plurality of sequencing reads from one or more samples from the human cancer subject that is representative of the cancer;
(B) determining a human leukocyte antigen (HLA) type of the human cancer subject;
(C) determining a plurality of clones, and for each respective clone α in the plurality of clones, an initial frequency Xα of the respective clone α in the one or more samples;
(D) for each respective clone α in the plurality of clones, computing a corresponding clone fitness score of the respective clone, thereby computing a plurality of clone fitness scores, each corresponding clone fitness score computed for a respective clone α by a first procedure comprising:
(a) identifying a plurality of neoantigens in the respective clone α;
(b) computing a recognition potential of each respective neoantigen in the plurality of neoantigens in the respective clone α by a second procedure comprising:
(i) computing an amplitude A of the respective neoantigen as a function of the relative major histocompatibility complex (MHC) affinity of the respective neoantigen and the wildtype counterpart of the respective neoantigen given the HLA type of the subject,
(ii) computing a probability of T-cell receptor recognition R of the respective neoantigen as a probability that the respective neoantigen is bound by T-cells that are specific to one or more known epitopes in a plurality of epitopes after class I MHC presentation, wherein the plurality of epitopes comprises 1×106 epitopes,
wherein the probability of T-cell receptor recognition R is computed as:

OG Complex Work Unit Math
wherein
a is a number that represents a horizontal displacement of a binding curve for the respective neoantigen,
k is a number that sets the steepness of the binding curve at a,
Z(k) is a partition function over the unbound state and all bound states of the respective neoantigen of the form

OG Complex Work Unit Math
wherein,
D is the plurality of epitopes,
each respective epitope e is an epitope from the plurality of epitopes that is positively recognized by T-cells after class I MHC presentation, and
|s, e| is a measure of sequence similarity between the respective neoantigen s and the respective epitope e, and
(iii) computing the recognition potential of the respective neoantigen as a function of the amplitude A of the respective neoantigen and the probability of T-cell receptor recognition R of the respective neoantigen; and
(c) determining the corresponding clone fitness score of the respective clone α as an aggregate of the neoantigen recognition potentials across the plurality of neoantigens in the respective clone α;
(E) computing a total fitness for the one or more samples as a sum of the clone fitness scores across the plurality of clones, wherein:
each clone fitness score is weighted by the initial frequency Xα of the corresponding clone α, and
the total fitness quantifies the likelihood that the human subject afflicted with the cancer will be responsive to the treatment regimen; and
(F) administering ipilimumab or tremelimumab to the subject when the total fitness of the subject is lower than a predetermined threshold.