IP Library › Granted Patent US 12,205,675
Granted Patent B2
US 12,205,675 · App. 16/920,641 · Granted Jan 21, 2025

Techniques for bias correction in sequence data

Inventors: Ekaterina Nuzhdina (Moscow, RU); Alexander Bagaev (Moscow, RU); Maksim Chelushkin (Moscow, RU); Yaroslav Lozinsky (Moscow, RU); Natalia Miheecheva (Moscow, RU); Aleksandr Zaitsev (Drozhzhino, RU)
Assignee: BostonGene Corporation
G16B30/10C12Q1/6806C12Q1/6869G16B20/00G16B20/20G16B25/10G16B35/10G16H50/30
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Quick Facts
Patent No.
US 12,205,675
App. No.
16/920,641
Granted
Jan 21, 2025
Kind
B2
Abstract

Described herein are various methods of collecting and processing of tumor and/or healthy tissue samples to extract nucleic acid and perform nucleic acid sequencing. Also described herein are various methods of processing nucleic acid sequencing data to remove bias from the nucleic acid sequencing data. Also described herein are various methods of evaluating the quality of nucleic acid sequence information. The identity and/or integrity of nucleic acid sequence data is evaluated prior to using the sequence information for subsequent analysis (for example for diagnostic, prognostic, or clinical purposes). The methods enable a subject, doctor, or user to characterize or classify various types of cancer precisely, and thereby determine a therapy or combination of therapies that may be effective to treat a cancer in a subject based on the precise characterization.

Claims (23)

1. A method comprising:

obtaining nucleic acid data comprising:

sequence data indicating a nucleotide sequence for at least 5 kilobases (kb) of DNA and/or RNA from a previously obtained biological sample of a subject, wherein the subject has cancer; and,

asserted information comprising information indicative of the following features:

(i) an asserted MHC genotype of the subject;

(ii) as asserted tumor type of the biological sample; and

(iii) an asserted sequencing platform used to generate the sequence data;

processing the nucleic acid data to obtain validated nucleic acid data, the processing comprising:

(a) performing MHC allele analysis on the sequence data;

(b) performing tumor type classification on the sequence data;

(c) performing RNA-seq type classification on the sequence data; and

(d) identifying the nucleic acid data as validated nucleic acid data when information obtained from performing (a)-(d) matches the asserted information;

using the validated nucleic acid data as input in a method for determining a molecular functional (MF) profile for the subject by determining, using the validated nucleic acid data, a gene group expression level for a gene group associated with cancer malignancy and a different gene group associated with cancer microenvironment,

wherein the gene group associated with cancer malignancy includes the following genes: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL

wherein the gene group associated with cancer microenvironment includes the following genes: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, LAMP3, HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, FASLG, PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG, IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1;

and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising:

a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by:

determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing gene group expression levels for a gene group associated with cancer malignancy and a different gene group associated with cancer microenvironment,

wherein the gene group associated with cancer malignancy includes the following genes: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL

wherein the gene group associated with cancer microenvironment includes the following genes: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, LAMP3, HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSFI3C, CD27, CD24, CR2, TNFRSFI7, TNFRSFI3B, CD22, CD79A, CD79B, BLK, NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, FASLG, PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG, IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSFIR, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; and

clustering the plurality of MF profiles to obtain the MF profile clusters;

clustering the MF profile of the subject with the plurality of MF profile clusters and determining that the subject has an inflamed/non-vascularized MF profile; and

administering to the subject an immune checkpoint blockade therapy to the subject having the inflamed/non-vascularized MF profile.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 6, 2021
From: NUZHDINA, EKATERINA; BAGAEV, ALEXANDER; CHELUSHKIN, MAKSIM; LOZINSKY, YAROSLAV; MIHEECHEVA, NATALIA; ZAITSEV, ALEXANDER
To: BOSTONGENE LLC
Reel/Frame 054823/0198 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 6, 2021
From: BOSTONGENE LLC
To: BOSTONGENE CORPORATION
Reel/Frame 054823/0247 →
Continuity (3)
Provisional Application 62991570 · Mar 18, 2020
Provisional Application 62870622 · Jul 3, 2019
Related Publication 20210005284A1 · Jan 7, 2021
References Cited (169)
US 4777127A · Suni et al. · 1988 [cited by applicant]
US 5219740A · Miller et al. · 1993 [cited by applicant]
US 5422120A · Kim · 1995 [cited by applicant]
US 5580859A · Felgner et al. · 1996 [cited by applicant]
US 5814482A · Dubensky, Jr. et al. · 1998 [cited by applicant]
US 5981568A · Kunz et al. · 1999 [cited by applicant]
US 20130184999A1 · Ding · 2013 [cited by applicant]
US 20180357372A1 · Bagaev et al. · 2018 [cited by applicant]
US 20200098448A1 · Shah et al. · 2020 [cited by applicant]
US 20210005283A1 · Nuzhdina et al. · 2021 [cited by applicant]
US 20220119881A1 · Nuzhdina et al. · 2022 [cited by applicant]
CN 105349617A · 2016 [cited by applicant]
EP 0345242A2 · 1989 [cited by applicant]
EP 0524968A1 · 1993 [cited by applicant]
GB 2200651A · 1988 [cited by applicant]
JP 2005505267A · 2005 [cited by applicant]
JP 2019528697A · 2019 [cited by applicant]
WO WO9007936A1 · 1990 [cited by applicant]
WO WO9011092A1 · 1990 [cited by applicant]
WO WO9102805A2 · 1991 [cited by applicant]
WO WO9114445A1 · 1991 [cited by applicant]
WO WO9303769A1 · 1993 [cited by applicant]
WO WO9310218A1 · 1993 [cited by applicant]
WO WO9311230A1 · 1993 [cited by applicant]
WO WO9319191A1 · 1993 [cited by applicant]
WO WO9325234A1 · 1993 [cited by applicant]
WO WO9325698A1 · 1993 [cited by applicant]
WO WO9403622A1 · 1994 [cited by applicant]
WO WO9412649A2 · 1994 [cited by applicant]
WO WO9423697A1 · 1994 [cited by applicant]
WO WO9428938A1 · 1994 [cited by applicant]
WO WO9500655A1 · 1995 [cited by applicant]
WO WO9507994A2 · 1995 [cited by applicant]
WO WO9511984A2 · 1995 [cited by applicant]
WO WO9513796A1 · 1995 [cited by applicant]
WO WO9530763A2 · 1995 [cited by applicant]
WO WO9617072A2 · 1996 [cited by applicant]
WO WO9742338A1 · 1997 [cited by applicant]
WO WO0053211A2 · 2000 [cited by applicant]
WO WO2018231762A1 · 2018 [cited by applicant]
WO WO2018231771A1 · 2018 [cited by applicant]
Trowsdale et al. (Annual Reviews in Genomics and Human Genetics (2013) vol. 14:301-323). [cited by examiner]
U.S. Appl. No. 16/920,636, filed Jul. 3, 2020, Nuzhdina et al. [cited by applicant]
PCT/US2020/040834, Oct. 21, 2020, Invitation to Pay Additional Fees. [cited by applicant]
PCT/US2020/040834, Dec. 15, 2020, International Search Report and Written Opinion. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2020/040834 mailed Dec. 15, 2020. [cited by applicant]
Invitation to Pay Additional Fees for International Application No. PCT/US2020/040834 mailed Oct. 21, 2020. [cited by applicant]
[No Author Listed], How to Prepare a Single-Cell Suspension from Primary Tissue Samples (e.g. Mouse Spleen). YouTube, STEMCELL Technologies. Sep. 24, 2012:13 pages. https://www.youtube.com/watch?v=N0jftyYqM38 [last acce… [cited by applicant]
[No Author Listed], 10x Red Blood Cell (RBC) Lysis Buffer (ab204733). Abcam plc. 2021:3 pages. https://www.abcam.com/10x-red-blood-cell-rbc-lysis-buffer-ab204733.html [last accessed Jan. 19, 2021]. [cited by applicant]
[No Author Listed], About the European Nucleotide Archive. European Nucleotide Archive. 2020:3 pages. https://www.ebi.ac.uk/ena/browser/about [last accessed Dec. 14, 2020]. [cited by applicant]
[No Author Listed], BBMap Guide. DOE Joint Genome Institute (JGI). 2021:9 pages. https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/ [last accessed Feb. 2, 2021]. [cited by applicant]
[No Author Listed], BioFiles for Life Science Research. Sigma-Aldrich. 2006;2:28 pages. https://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/General_Information/2/biofiles_issue2.pdf [last accessed Dec. 14,… [cited by applicant]
[No Author Listed], CaptureSeq. Illumina, Inc. 2021:1 page. https://sapac.illumina.com/science/sequencing-method-explorer/kits-and-arrays/captureseq.html [last accessed Jan. 19, 2021]. [cited by applicant]
[No Author Listed], Cell Dissociation Buffer, enzyme-free, Hanks' Balanced Salt Solution, Thermo Fisher Catalog No. 13150016. Thermo Fisher Scientific. 2021:3 pages. https://www.thermofisher.com/order/catalog/product/13… [cited by applicant]
[No Author Listed], Cell Dissociation Buffer, enzyme-free, PBS, Thermo Fisher Catalog No. 13151014. Thermo Fisher Scientific. 2021:4 pages. https://www.thermofisher.com/order/catalog/product/13151014?us&en#/13151014?us&… [cited by applicant]
[No Author Listed], ContEst. Broad Institute Cancer Genome Analysis. 2013:1 page. http://software.broadinstitute.org/cancer/cga/contest [last accessed Dec. 22, 2020]. [cited by applicant]
[No Author Listed], EMBL-EBI, the home for big data in biology. EMBL-EBI. 2020:4 pages. https://www.ebi.ac.uk/ [last accessed Dec. 14, 2020]. [cited by applicant]
[No Author Listed], Enzyme Free Cell Dissociation Solution PBS Based (1X), liquid, 500ml, Millipore Sigma Aldrich catalog No. S-014-B. Millipore Sigma. 2020:3 pages. https://www.emdmillipore.com/US/en/product/Enzyme-Fre… [cited by applicant]
[No Author Listed], For all you seq . . . Illumina, Inc. 2015:2 pages. https://www.illumina.com/content/dam/illumina-marketing/documents/applications/ngs-library-prep/ForAllYouSeqMethods.pdf [last accessed Feb. 2, 2021]. [cited by applicant]
[No Author Listed], GenBank Overview. National Center for Biotechnology Information. Oct. 20, 2020:2 pages. https://www.ncbi.nlm.nih.gov/genbank/ [last accessed Dec. 14, 2020]. [cited by applicant]
[No Author Listed], GenCode. EMBL-EBI. 2020:2 pages. https://www.gencodegenes.org/ [last accessed Dec. 14, 2020]. [cited by applicant]
[No Author Listed], Genome Analysis Toolkit. GATK. 2021:3 pages. https://gatk.broadinstitute.org/hc/en-us [last accessed Feb. 2, 2021]. [cited by applicant]
[No Author Listed], LifeScience. Roche Molecular Systems, Inc. Sep. 16, 2020:7 pages. https://www.lifescience.roche.com/en_us.html [last accessed Jan. 15, 2021]. [cited by applicant]
[No Author Listed], PacBio. Pacific Biosciences of California, Inc. 2021:6 pages. https://www.pacb.com/ [last accessed Jan. 28, 2021]. [cited by applicant]
[No Author Listed], QIAsymphony SP/AS instruments. Qiagen. 2020:8 pages. https://www.qiagen.com/us/products/instruments-and-automation/pcr-setup-liquid-handling/qiasymphony-spas-instruments/#orderinginformation [last ac… [cited by applicant]
[No Author Listed], RefSeq: NCBI Reference Sequence Database. National Center for Biotechnology Information. Jan. 8, 2021:1 page. https://www.ncbi.nlm.nih.gov/refseq/ [last accessed Jan. 19, 2021]. [cited by applicant]
[No Author Listed], Release 23. EMBL-EBI. 2020:8 pages. https://www.gencodegenes.org/human/release_23.html [last accessed Dec. 14, 2020]. [cited by applicant]
[No Author Listed], SeQuiLa User Guide. biodatageeks.org. May 22, 2019:3 pages. https://web.archive.org/web/20191229100046/biodatageeks.org/sequila/ [last accessed Dec. 21, 2020]. [cited by applicant]
[No Author Listed], SureSelect Clinical Research Exome V2. Agilent Technologies, Inc. 2021:4 pages. https://www.agilent.com/en/product/next-generation-sequencing/hybridization-based-next-generation-sequencing-ngs/exome-… [cited by applicant]
[No Author Listed], SureSelect Human All Exon V6. Agilent Technologies. May 14, 2015:2 pages. https://www.agilent.com/cs/library/datasheets/public/SureSelect%20V6%20DataSheet%205991-5572EN.pdf [last accessed Dec. 14, 20… [cited by applicant]
[No Author Listed], TapeStation Systems. Agilent Technologies, Inc. 2021:1 page. https://www.agilent.com/en/product/automated-electrophoresis/tapestation-systems?gclid=CjwKCAiAo5qABhBdEiwAOtGmbg30JCRqBJgosCgVVTy1885MhWp… [cited by applicant]
[No Author Listed], Thermo Fisher Scientific. Thermo Fisher Scientific. 2021:2 pages. https://www.thermofisher.com/us/en/home.html [last accessed Jan. 19, 2021]. [cited by applicant]
[No Author Listed], VerifyBamID. Center for Statistical Genetics. Sep. 9, 2017:8 pages. https://genome.sph.umich.edu/wiki/VerifyBamID [last accessed Jan. 19, 2021]. [cited by applicant]
[No Author Listed], Whole Exome Sequencing Guide. Genohub, Inc. 2019:6 pages. https://genohub.com/exome-sequencing-library-preparation/ [last accessed Dec. 22, 2020]. [cited by applicant]
[No Author Listed], XGBoost Documentation. Xgboost developers. 2020:2 pages. https://xgboost.readthedocs.io/en/latest/ [last accessed Dec. 22, 2020]. [cited by applicant]
Andrews, FastQC. Babraham Institute. Jan. 8, 2019:6 pages. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ [last accessed Dec. 22, 2020]. [cited by applicant]
Bagnoli et al., Studying cancer heterogeneity by single-cell RNA sequencing. Methods in Molecular Biology. 2019:305-319. [cited by applicant]
Behbehani, Applications of mass cytometry in clinical medicine: the promise and perils of clinical CyTOF. Clinics in Laboratory Medicine. Dec. 1, 2017;37(4):945-64. [cited by applicant]
Bray et al., Near-optimal probabilistic RNA-seq quantification. Nature biotechnology. May 2016;34(5):525-7. [cited by applicant]
Brodin, The biology of the cell-insights from mass cytometry. The FEBS journal. Apr. 2019;286(8):1514-22. [cited by applicant]
Clark et al., Subtractive Hybridization. Elsevier B.V .. 2020:18 pages. https://www.sciencedirect.com/topics/immunology-and-microbiology/subtractive-hybridization [last accessed Dec. 22, 2020]. [cited by applicant]
Connelly et al., In vivo gene delivery and expression of physiological levels of functional human factor VIII in mice. Human gene therapy. Feb. 1, 1995;6(2):185-93. [cited by applicant]
Curiel et al., High-efficiency gene transfer mediated by adenovirus coupled to DNA-polylysine complexes. Human gene therapy. Apr. 1, 1992;3(2):147-54. [cited by applicant]
De Wildt et al., Characterization of human variable domain antibody fragments against the U1 RNA?associated A protein, selected from a synthetic and a patient?derived combinatorial V gene library. European journal of im… [cited by applicant]
Enblad et al., CAR T-cell therapy: the role of physical barriers and immunosuppression in lymphoma. Human gene therapy. Aug. 1, 2015;26(8):498-505. [cited by applicant]
Ewing et al., Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome research. Mar. 1, 1998;8(3):186-94. [cited by applicant]
Ewing et al., Base-calling of automated sequencer traces usingPhred. I. Accuracy assessment. Genome research. Mar. 1, 1998;8(3):175-85. [cited by applicant]
Findeis et al., Targeted delivery of DNA for gene therapy via receptors. Trends in biotechnology. May 1, 1993;11(5):202-5. [cited by applicant]
Galli et al., The end of omics? High dimensional single cell analysis in precision medicine. European journal of immunology. Feb. 2019;49(2):212-20. [cited by applicant]
Gan et al., Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC medical genomics. Dec. 1, 2018;11(6):117. [cited by applicant]
Gomez-Acata et al., Methods for extracting'omes from microbialites. Journal of microbiological methods. May 1, 2019;160:1-10. [cited by applicant]
Gondhalekar et al., Alternatives to current flow cytometry data analysis for clinical and research studies. Methods. Feb. 1, 2018;134:113-29. [cited by applicant]
Heng et al., Comparison of enzymatic and non-enzymatic means of dissociating adherent monolayers of mesenchymal stem cells. Biological procedures online. Dec. 2009;11(1):161-9. [cited by applicant]
Huang et al., High throughput single cell RNA sequencing, bioinformatics analysis and applications. Advances in Experimental Medicine and Biology. 2018;1068:33-43. [cited by applicant]
Jazayeri et al., RNA-SEQ: a glance at technologies and methodologies. Acta Biológica Colombiana. May 2015;20(2):23-35. [cited by applicant]
Kaplitt et al., Long-term gene expression and phenotypic correction using adeno-associated virus vectors in the mammalian brain. Nature genetics. Oct. 1994;8(2):148-54. [cited by applicant]
Kashima et al., An Informative Approach to Single-Cell Sequencing Analysis. Advances in Experimental Medicine and Biology. 2019;1129:81-96. [cited by applicant]
Kimura et al., Retroviral delivery of DNA into the livers of transgenic mice bearing premalignant and malignant hepatocellular carcinomas. Human gene therapy. Jul. 1, 1994;5(7):845-52. [cited by applicant]
Kiselev et al., hemberg-lab/scRNA.seq.course. GitHub. Oct. 15, 2020:5 pages. https://github.com/hemberg-lab/scRNA.seq.course [last accessed Jan. 19, 2021]. [cited by applicant]
Kulkarni et al., Beyond bulk: a review of single cell transcriptomics methodologies and applications. Current opinion in biotechnology. Aug. 1, 2019;58:129-36. [cited by applicant]
Macaulay et al., Single-cell multiomics: multiple measurements from single cells. Trends in Genetics. Feb. 1, 2017;33(2):155-68. [cited by applicant]
Melsted et al., kallisto. GitHub. Feb. 12, 2020:3 pages. https://github.com/pachterlab/kallisto [last accessed Dec. 14, 2020]. [cited by applicant]
Millis et al., Strand-specific RNA-seq provides greater resolution of transcriptome profiling. Current genomics. May 1, 2013;14(3):173-81. [cited by applicant]
Moore et al., Isolation and Purification of DNA. In: Molecular Biology. Current Protocols in Immunology. Chapter 10:2001:10 pages. [cited by applicant]
Olsen et al., The anatomy of single cell mass cytometry data. Cytometry Part A. Feb. 2019;95(2):156-72. [cited by applicant]
Pedersen et al., mosdepth. Github. Sep. 29, 2020:13 pages. https://github.com/brentp/mosdepth [last accessed Dec. 14, 2020]. [cited by applicant]
Pereira et al., RNA-seq: From reads to counts. Github. Jul. 27, 2015:1-4. http://bioinformatics-core-shared-training.github.io/cruk-bioinf-sschool/Day2/rnaSeq_align.pdf [last accessed Feb. 2, 2021]. [cited by applicant]
Petrova et al., Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Scientific reports. Jan. 24, 2017;7(1):1-15. [cited by applicant]
Pfeifer, From next-generation resequencing reads to a high-quality variant data set. Heredity. Feb. 2017;118(2):111-24. [cited by applicant]
Philip et al., Efficient and sustained gene expression in primary T lymphocytes and primary and cultured tumor cells mediated by adeno-associated virus plasmid DNA complexed to cationic liposomes. Molecular and Cellular… [cited by applicant]
Quatromoni et al., An optimized disaggregation method for human lung tumors that preserves the phenotype and function of the immune cells. Journal of leukocyte biology. Jan. 2015;97(1):201-9. [cited by applicant]
Rani, Major Histocompatibility Complex (MHC), Applications. In: Dubitzky et al., Eds. Encyclopedia of systems biology. Springer Publishing Company, Incorporated. Jan. 1, 2013:10 pages. [cited by applicant]
Scheimer, Illumina TruSeq DNA Adapters De-Mystified. Tufts University Core Facility. 2011:1-5. http://tucf-genomics.tufts.edu/documents/protocols/TUCF_Understanding_Illumina_TruSeq_Adapters.pdf [last accessed Dec. 22, 2… [cited by applicant]
Seki et al., Single-cell DNA-seq and RNA-seq in cancer using the C1 system. Advances in Experimental Medicine and Biology. 2019;1129:27-50. [cited by applicant]
Shelton et al., nygenome/Conpair. GitHub. Jun. 24, 2018:6 pages. https://github.com/nygenome/Conpair [last accessed Dec. 14, 2020]. [cited by applicant]
Soares et al., Go with the flow: advances and trends in magnetic flow cytometry. Analytical and bioanalytical chemistry. Mar. 16, 2019;411(9):1839-62. [cited by applicant]
Sun et al., Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells. Oncotarget. Feb. 16, 2018;9(13):10945-61. [cited by applicant]
Vaught et al., Biological sample collection, processing, storage and information management. IARC Sci Publ. 2011;163:23-42. [cited by applicant]
Vaught et al., Biospecimens and biorepositories: from afterthought to science. Cancer Epidemiol Biomarkers Prev. Feb. 2012;21(2):253-5. [cited by applicant]
Wagner et al., Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory in biosciences. Dec. 1, 2012;131(4):281-5. [cited by applicant]
Wang, RSeQC: An RNA-seq Quality Control Package. Liguo Wang. Aug. 21, 2020:41 pages. http://rseqc.sourceforge.net/ [last accessed Jan. 19, 2021]. [cited by applicant]
Wingett et al., Introduction. Babraham Institute. 2019:9 pages. http://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/_build/html/index.html [last accessed Dec. 22, 2020]. [cited by applicant]
Wingett, FastQ Screen. Babraham Institute. Jun. 17, 2019:5 pages. https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/ [last accessed Dec. 14, 2020]. [cited by applicant]
Woffendin et al., Nonviral and viral delivery of a human immunodeficiency virus protective gene into primary human T cells. Proceedings of the National Academy of Sciences. Nov. 22, 1994;91(24):11581-5. [cited by applicant]
Wu et al., Incorporation of adenovirus into a ligand-based DNA carrier system results in retention of original receptor specificity and enhances targeted gene expression. Journal of Biological Chemistry. Apr. 15, 1994;2… [cited by applicant]
Wu et al., Receptor-mediated gene delivery and expression in vivo. Journal of Biological Chemistry. Oct. 15, 1988;263(29):14621-4. [cited by applicant]
Wu et al., Receptor-mediated gene delivery in vivo. Partial correction of genetic analbuminemia in Nagase rats. Journal of Biological Chemistry. Aug. 5, 1991;266(22):14338-42. [cited by applicant]
Wu et al., Targeting genes: delivery and persistent expression of a foreign gene driven by mammalian regulatory elements in vivo. Journal of Biological Chemistry. Oct. 15, 1989;264(29):16985-7. [cited by applicant]
Zenke et al., Receptor-mediated endocytosis of transferrin-polycation conjugates: an efficient way to introduce DNA into hematopoietic cells. Proceedings of the National Academy of Sciences. May 1, 1990;87(10):3655-9. [cited by applicant]
Zilionis et al., Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity. May 21, 2019;50(5):1317-34. [cited by applicant]
PCT/IB2020/000928, Feb. 9, 2021, Invitation to Pay Additional Fees. [cited by applicant]
PCT/IB2020/000928, Mar. 30, 2021, International Search Report and Written Opinion. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/IB2020/000928 mailed Mar. 30, 2021. [cited by applicant]
Invitation to Pay Additional Fees for International Application No. PCT/IB2020/000928 mailed Feb. 9, 2021. [cited by applicant]
[No Author Listed], SOLiD® Total RNA-Seq Kit. Life Technologies. Jul. 2011:1-106. 106 pages. http://tools.thermofisher.com/content/sfs/manuals/cms_078610.pdf [last accessed Dec. 22, 2020]. [cited by applicant]
Conesa et al., A survey of best practices for RNA-seq data analysis. Genome biology. Jan. 26, 2016;17(1):13. 19 pages. [cited by applicant]
Fisher et al., A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome biology. Jan. 4, 2011;12(1):R1. 15 pages. [cited by applicant]
Imbeaud et al., Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nucleic acids research. Mar. 30, 2005;33(6):e56. 12 pages. [cited by applicant]
Larjo et al., Accuracy of programs for the determination of human leukocyte antigen alleles from next-generation sequencing data. Frontiers in Immunology. Dec. 13, 2017;8:1815. 9 pages. [cited by applicant]
Luecken et al., Current best practices in single?cell RNA?seq analysis: a tutorial. Molecular systems biology. Jun. 2019;15(6):e8746. 23 pages. [cited by applicant]
Macosko et al., Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. May 21, 2015;161(5):1202-14. 107 pages. [cited by applicant]
Mestan et al., Genomic sequencing in clinical trials. Journal of translational medicine. Dec. 30, 2011;9(1):222. 10 pages. [cited by applicant]
Pennartz et al., Generation of single-cell suspensions from mouse neural tissue. JoVE (Journal of Visualized Experiments). Jul. 7, 2009;(29):e1267. 4 pages. [cited by applicant]
See et al., A single-cell sequencing guide for immunologists. Frontiers in immunology. Oct. 23, 2018;9:2425. 13 pages. [cited by applicant]
Vento-Tormo et al., Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. Nov. 2018;563(7731):347-53. 26 pages. [cited by applicant]
Woo et al., Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines. BMC medical genomics. Jul. 1, 2019;12(1):92. 53 pages. [cited by applicant]
[No Author Listed], Vault RNA. Wikipedia. Nov. 29, 2021. https://en.wikipedia.org/wiki/Vault_RNA#:˜:text=The%20vault%20complex%20comprises%20the,transcribed%20by%20RNA%20polymerase%20III [Last accessed Jan. 12, 2022]. [cited by applicant]
[No Author Listed], Automatic annotation of non-coding genes. US East Ensembl. Dec. 2021. http://useast.ensembl.org/info/genome/genebuild/ncrna.html [Last accessed Jan. 12, 2022]. [cited by applicant]
[No Author Listed], Gene/Transcript Biotypes in GENCODE & Ensembl. Gencode. 2020. https://www.gencodegenes.org/pages/biotypes.html [Last accessed Jan. 12, 2022]. [cited by applicant]
Albini et al., Metastasis signatures: genes regulating tumor-microenvironment interactions predict metastatic behavior. Cancer and Metastasis Reviews. Mar. 2008;27(1):75-83. [cited by applicant]
Byron et al., Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nature Reviews Genetics. May 2016;17(5):257-71. [cited by applicant]
Chen et al., New horizons in tumor microenvironment biology: challenges and opportunities. BMC medicine. Dec. 2015;13(1):1-4. [cited by applicant]
Chen et al., Turning foes to friends: targeting cancer-associated fibroblasts. Nature reviews Drug discovery. Feb. 2019;18(2):99-115. [cited by applicant]
Jia et al., Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging (Albany NY). Apr. 2018;10(4):592. [cited by applicant]
Ma et al., On the classification of long non-coding RNAs. RNA biology. Jun. 1, 2013;10(6):924-33. [cited by applicant]
Moncada et al., Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma. bioRxiv. Jan. 1, 2018:254375. [cited by applicant]
Ostrander, Pseudogene. National Human Genome Research Institute. 2021. https://www.genome.gov/genetics-glossary/Pseudogene [Last accessed Jan. 12, 2022]. [cited by applicant]
Singh et al., Increasing the complexity of chromatin: functionally distinct roles for replication-dependent histone H2A isoforms in cell proliferation and carcinogenesis. Nucleic acids research. Nov. 1, 2013;41(20):9284… [cited by applicant]
Vogt, Cancer genes. Western journal of medicine. Mar. 1993;158(3):273. [cited by applicant]
Klein et al., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. May 21, 2015;161(5):1187-201. [cited by applicant]
PCT/IB2020/000928, Jan. 13, 2022, International Preliminary Report on Patentability. [cited by applicant]
EP 20829631.9, Jun. 25, 2024, Communication pursuant to Article 94(3) EPC. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/IB2020/000928 mailed Jan. 13, 2022. [cited by applicant]
Communication pursuant to Article 94(3) EPC for European Application No. 20829631.9 dated Jun. 25, 2024. 6 pages. [cited by applicant]
Moncada et al., Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis. bioRxiv 254375; Mar. 13, 2019. 41 pages. doi: https://doi.org/10.1… [cited by applicant]
Nathanson et al., Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer immunology research. Jan. 1, 2017;5(1):84-91. Published online Dec. 12, 2016. [cited by applicant]
Ren et al., RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell research. May 2012;22(5):806-21. [cited by applicant]
Tarazona et al., Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic acids research. Dec. 2, 2015;43(21):e140-. 15 pages. [cited by applicant]
Van Allen et al., Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. Author Manuscript; available in PMC Oct. 7, 2016. Published in final edited form as: Oct. 9, 2015;350(6257):207-11. [cited by applicant]