Methods of identifying a subject with long telomere length

Abstract

Provided herein are methods of identifying a subject as having an increased risk of a cancer, the method including (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as having an increased risk of the cancer.

Description

[0001] METHODS OF IDENTIFYING A SUBJECT WITH LONG TELOMERE LENGTH

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority’ to U.S. Provisional Patent Application No. 63 / 463,226, filed on May 1, 2023, which is incorporated herein by reference in its entirety.

[0004] FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0005] This invention was made with government support under grant CAI 60433 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0006] TECHNICAL FIELD

[0007] The present disclosure relates to the area of biotechnology, and more specifically, to measurement of mammalian telomere lengths.

[0008] BACKGROUND

[0009] Telomere shortening is a well-characterized cellular aging mechanism, and short telomere syndromes are the most common progeria. The length of telomere repeats, the tandem (TTAGGG)n sequences at chromosome ends, predicts the onset of replicative senescence and functions as a mitotic clock. Telomeres shorten with cell division because of the end replication problem, and short telomeres signal a DNA damage response that promotes cellular senescence and apoptosis. Germline loss-of-function mutations in genes that encode components of telomerase, the enzy me which synthesizes new telomere repeats, cause a short telomere progeria that is marked by pulmonary’ and hematopoietic disease. Its most common manifestation is idiopathic pulmonary fibrosis, which co-occurs with syndromic features including premature hair graying, bone marrow failure, and B and T cell immunodeficiency. Because heterozy gous mutations in telomerase genes are prevalent in adults with pulmonary’ fibrosis, the short telomere syndromes are more common than other progeria such as Hutchinson-Gilford and ataxia telangiectasia. Long telomeres extend the replicative potential of cultured cells, raising the possibility that mutations that prevent telomere shortening may influence specific aging phenotypes and disease risk; however, whether such a cellular ‘pronearia' (from Greek nearos for young / youth) has clinical manifestations is not known.

[0010] The protection of telomeres 1, POTI, protein binds single-stranded 3' ends of telomeres. It is a conserved, essential gene that protects telomeres from exonuclease degradation. POTI has also been implicated in regulating telomerase elongation with some studies pointing to its role in promoting telomerase enzyme processivity. while others identify a role in negatively regulating telomerase catalytic activity. Heterozygous somatic POTI mutations were initially identified in chronic lymphocytic leukemia (CLL), and germline mutations were later reported in families with isolated cancers including melanoma, glioma, and CLL. Biallelic POTI mutations have been reported in a single case of a severe infant-onset syndrome with some short telomere features. However, the mechanisms underlying the risk of disease associated with heterozygous POTI mutations are not understood, and hypotheses have ranged from telomere deprotection, genome instability, telomere elongation, to telomere shortening.

[0011] SUMMARY

[0012] Provided herein are methods of identifying a subject as having an increased risk of a cancer, the method comprising: (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as having an increased risk of the cancer.

[0013] Also provided herein are methods of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer, the method comprising: (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as being in need of cancer screening for early detection or prevention of the cancer.

[0014] Also provided herein are methods of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer, the method comprising: (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) determining that the subject exhibits an increased T-cell receptor (TCR) or a B-cell receptor (BCR) clonahty than a reference subject; (d) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratify ing the subject based on the subject having long telomere length, the gene variant, increased TCR or BCR clonality, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject to be in need of cancer screening for early detection or prevention of the cancer.

[0015] In some embodiments, the gene variant comprises a POTI mutation, a TERT promoter mutation, an ACD / TPP1 mutation, aTINF2 mutation, aTERFl mutation, or a RAP 1 mutation.

[0016] In some embodiments, the determining step (a) comprises sequencing. In some embodiments, the sequencing comprises whole genome sequencing. In some embodiments, the determining step (a) further comprises flow cytometry and fluorescence in-situ hybridization (flow-FISH). In some embodiments, the long telomere length comprises a telomere length longer than an age-adjusted reference subject.

[0017] In some embodiments, detecting the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage comprises genome sequencing. In some embodiments, detecting the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage comprises one or more of assessing the number of independent clonal mutations, estimated onset, mutation burden, mutational signature, and / or overall clonality in the subject.

[0018] In some embodiments, the cancer is a benign solid or hematologic neoplasm. In some embodiments, the cancer comprises melanoma, thyroid cancer, lung cancer, neuronal cancer, glioma, parotid and parathyroid adenoma, angiosarcoma, osteosarcoma, soft tissue sarcoma, bladder cancer, kidney cancer, colorectal cancer, lymphoma, or leukemia. In some embodiments, the cancer is a malignant solid neoplasm. In some embodiments, the cancer comprises gliomas and other neuronal cancers, thyroid cancers, sarcoma, epithelial malignancies, colorectal cancer, renal cell cancer, or lung cancer.

[0019] Also provided herein are methods of identifying a subject as a T-cell or bone marrow donor, the method comprising: (a) determining that the subject exhibits a medium telomere length; (b) detecting the absence of a gene variant in the subj ect; (c) determining that the subj ect has a normal T-cell receptor (TCR) or aB-cell receptor (BCR) clonality than a reference subject; (d) detecting the absence of a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratifying the subject based on the subj ect having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as a T-cell or bone marrow donor.

[0020] Also provided herein are methods of stratifying a subject as a transplant recipient, the method comprising: (a) determining that the subject exhibits a medium telomere length; (b) detecting the absence of a gene variant in the subject; (c) determining that the subject has a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject; (d) detecting the absence of a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratifying the subject based on the subject having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as a transplant recipient. In some embodiments, the transplant comprises hematopoietic stem cell transplant.

[0021] In some embodiments, the gene variant comprises a POTI mutation, a TERT promoter mutation, an ACD / TPP1 mutation, aTINF2 mutation, aTERFl mutation, or a RAP 1 mutation.

[0022] In some embodiments, the determining step (a) comprises sequencing. In some embodiments, the sequencing comprises whole genome sequencing. In some embodiments, the determining step (a) further comprises flow cytometry and fluorescence in-situ hybridization (flow-FISH).

[0023] In some embodiments, detecting the absence of disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage comprises genome sequencing. In some embodiments, detecting the absence of disease-indicative phylogenetic pattern of the singlecolony hematopoietic lineage comprises one or more of assessing the number of independent clonal mutations, estimated onset, mutation burden, mutational signature, and / or overall clonality in the subject.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0025] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF DRAWINGS

[0026] FIGs. 1A-1B show telomere length and pedigrees of POTI mutation carriers. FIG. 1A shows telomere length measured by flow cytometry and fluorescence in situ hybridization in 13 living POTI mutation carriers and their unaffected relatives. Data are plotted relative to a clinically validated nomogram derived from healthy controls. FIG. IB shows pedigrees of POTI mutation carriers with probands noted by arrows and individuals with cancer history. Key denotes cancer diagnoses and carrier status, and bold identifiers refers to individuals with POTI genotypes.

[0027] FIGs. 2A-2F show benign and malignant neoplastic manifestations among POTI mutation carriers.

[0028] FIG. 2A shows a diagram showing diagnoses among 17 POTI mutation carriers and the age at last assessment with benign and malignant disease noted as in the key. d. refers to the age at death. Myeloid clonality refers to the clonal hematopoiesis of indeterminate potential (CHIP) data depicted in FIG. 2C (for 4 deceased individuals, CHIP and T cell clonality was not assessed). Sex is denoted below as male (M) and female (F). FIG. 2B shows representative images of benign melanosis and in FIG. 2C, a computed tomography-captured goiter (arrows). FIG. 2D shows an axial computed tomography image showing a left renal mass diagnosed as clear cell carcinoma (arrow). FIG. 2E shows a representative magnetic resonance image of pediatric-onset glioblastoma multiforme. FIG. 2F shows a range of benign to malignant diagnoses grouped by affected tissue among 17 POTI mutation carriers with the number of individuals affected in FIG. 2A shown.

[0029] FIGs. 3A-3H show lymphoid and myeloid clonal hematopoiesis predisposition among POTI mutation carriers. FIG. 3A shows hematoxylin and eosin images of small bow-el from an autopsy of a POTI mutation carrier who developed large T cell lymphoma after receiving immune checkpoint inhibitor therapy for metastatic melanoma. Lymphocyte infiltrates involving the lamina propria were positive for CD3. FIG. 3B show productive clonality score is plotted relative to age (calculated from T cell receptor-V CDR3 sequencing as 1 minus the normalized Shannon’s entropy for all productive rearrangements). The range is 0 to 1, where 0 represents poly clonality7and 1 indicates one rearrangement dominates the entire repertoire. Eighteen POTI mutation carriers are from 7 unrelated families with red and rose colors referring to the original and validation cohorts, respectively. Percentile lines are derived from 258 healthy bone marrow donors who are seropositive for cytomegalovirus. An individual with history7of cutaneous T cell lymphoma but no known systemic disease is denoted by . FIG. 3C shows clonal hematopoiesis of indeterminate potential co-mutation plot among POTI mutation carriers and their relatives sorted by increasing age with germline mutation denoted below. JAK2 ‘GGCC’ haplotype status is also color-coded in the key. *These three individuals had additional DNMT3A 1-2% VAF mutations and are listed in FIG. 3D. FIG. 3D shows DNMT3A mutations identified in POTI mutation carriers and their relatives in are shown relative to the protein’s conserved domains and their allele frequency. Full circle denotes nonsynonymous mutations and the empty circle a splicing variant. FIG. 3E shows JAK2N6V VAF identified by targeted ultradeep sequencing plotted relative to telomere length in POTI mutation carriers and their relatives. FIGs. 3F-3H show phylogenies of single cell-derived hematopoietic colonies shown on a somatic variant scale (single nucleotide variants and short insertions / deletions). Age, telomere length (TL) percentile along with the POTI genotype are shown above each tree (age reflects time of draw with all 3 individuals shown in FIG. 3C). Two de novo JAK2 mutations are resolved in the tree in FIG. 3G, and the two co-occurring DNMT3A mutations in panel H were found to be biallelic in phasing studies. Tree topolog)' in the two POTI mutation carriers shows the tips, which represent contemporary hematopoietic colonies, descended from fewer ancestral progenitor lineages (internal branches), in contrast to the tree of the related individual in FIG. 3F. The number of somatic variants in POTI mutation carriers is also higher even in clades without detectable driver mutations.

[0030] FIGs. 4A-4E show genetic anticipation and longitudinal telomere length changes among POTI mutation earners. FIGs. 4A-4C show age of onset in three parent-child pairs showing earlier cancer diagnoses in successive generations. Mutation carriers (shaded) carry the p.R273Q (left and right pedigrees), and in the middle pedigree, they carry' p.I78T. FIGs. 4B-4C show difference in lymphocyte and granulocyte telomere length relative to the median for age as depicted at study enrollment and at follow-up two years later. Data are shown for POTI mutation carriers and their siblings or children who are non-carriers. FIG. 4D shows mean absolute change in absolute telomere length over a 2-year period in POTI mutation carriers (red) and their relatives (blue). Error bars reflect standard error of the mean. P-values in FIGs. 4B-4D are adjusted for multiplicity and calculated by the Mann-Whitney test. FIG. 4E shows mean variant allele frequency (VAF) for a somatic heterozygous driver mutation acquired at birth over a lifetime in telomere length groups calculated from 10,000 simulations for each group. Dashed line refers to contemporary 2% VAF threshold defining clonal hematopoiesis of indeterminate potential (CHIP).

[0031] FIGs. 5A-5D show estimated age of onset of driver clonal hematopoiesis mutation in three individuals. FIGs. 5A-5C show time-calibrated phylogenetic trees with POTI mutation status and telomere length noted above and age on the right. The estimated onset of each somatic driver mutation is annotated above each tree, respectively. Somatic mutations in the two POTI mutation carriers in FIGs. 5B and 5C arose earlier than in the related POT 1 wildtype individual in FIG. 5A. The tree topology in the two POTI mutation carriers (FIGs. 5B-5C) also shows oligoclonality with fewer branches arising in the first decade of life, in contrast to the tree in the related individual in FIG. 5A. Two JAK2V617F mutations arose independently in FIG. 5B. and the two DNMT3A mutations in FIG. 5C were found to be biallelic in phasing studies. In FIG. 5A, * refers to aN0TCH2 genotype that was inferred by PCR amplification and Sanger sequencing due to low sequencing coverage at that single nucleotide variant in that colony. FIG. 5D is a plot of the mean and standard error of the number of somatic mutations for the sequenced single cell-derived colonies. The data show a higher mean and wider distribution in the two POTI mutation carriers compared to the related POTI wildtype individual.

[0032] FIGs. 6A-6C show time-scaled phylogenies of hematopoietic clones annotated for coding mutations in 87 CH-driver genes. FIGs. 6A-6C show time-calibrated phylogenetic trees with POTI mutation status and telomere length noted above and age on the right. Below each tree are the protein-coding mutations, with genes labeled to the left, protein coding effect on the right, and colony identifiers below each matrix. FIG. 6A reflects an individual without a POTI mutation who carriers a non-canonical N0TCH2 clonal mutation (p.V1003*) in the ancestral lineage to five contemporary samples (variant in A39 was confirmed with PCR and Sanger sequencing), as well as two additional and independent mutations outside the clade. FIGs. 6B- 6C show trees for two POTI mutation carriers. FIG. 6B shows convergent JAK2 p.V617F single nucleotide variants (SNVs), one in an ancestral lineage to four contemporary samples, and the second mutation occurring in an independently evolving lineage; three other proteincoding mutation events are shown, one overlapping with the shared JAK2 mutation. FIG. 6C shows four DNMT3A mutations, two SNVs co-occurring in the ancestral lineage to 23 contemporary samples, and two additional mutations identified outside the clade. An EP300 p.P708* frame-shift insertion and JAK2 p.V617F also emerged within the large DNMT3A mutant clade. The list of clonal hematopoiesis genes annotated is in Table 3.

[0033] FIG. 7 shows somatic single base substitution (SBS) COSMIC signature per clone in two POTI mutation carriers and a related control who does not carry the mutation. Data are derived from single cell colony whole genome sequence data. The number of mutations per clone is higher in POTI mutation carriers and the predominant COSMIC signature is that associated with clock-like DNA replication errors as are seen with aging (C>T transitions). The clones with driver mutations noted by * above (e.g., clones with somatic DNMT3A mutations especially) have additional secondary signatures.

[0034] FIGs. 8A-8B show simulated dynamic of mean variant allele frequent (VAF) of single heterozygous driver mutation acquired at birth and over a lifetime. FIG. 8A shows mean lines with 95% confidence intervals shown in the shaded areas respectively for each group shown in the key. Data reflect 10,000 simulations for each group. Telomere loss in all the POTI wildtype groups in FIG. 8A was simulated at -100 bp / cell division. FIG. 8B shows analysis of varied telomere length gain in the simulation for POTI mutation carriers. Key shows initial telomere length at birth with the net telomere length gain / loss per cell division simulated for each group. Data in FIG. 8B are from 100 simulations for each parameter graphed.

[0035] FIG. 9 shows mean variant allele frequency (VAF) of a clonal hematopoiesis driver mutation acquired at birth, across 10,000 simulations for each of four groups graphed separately. The panel in the top left comer shows the mean VAF for 10,000 simulations for the POTI mutation group allowing for a +50 base pair gain with each cell division, while POTI wildty pe simulations allowed for -100 base pair loss with each cell division.

[0036] FIG. 10 shows proportion of simulated individuals with clonal hematopoiesis of indeterminate potential (CHIP). CHIP was defined using standard >2% variant allele frequency (VAF) criteria, and proportions were calculated from 10,000 simulations for each of the four groups.

[0037] FIGs. 11A-11B show read alignment and unfiltered variant call statistics for whole genome sequence data. FIG. 11A shows aggregated chromosomal read mapping statistics for colony and bulk sequence data for each individual. Data are shown for 51 whole genomes sequenced (1 bulk and 50 colonies) except for Individual C for whom there was sequence data for 1 bulk and 49 colonies (1 sample failed sequencing). FIG. 11B show s allele frequency spectra for all unfiltered variants aggregated across clones per individual.

[0038] FIGs. 12A-12C show summary of variant filtering data for Individuals A, B and C, respectively. Each panel shows each filter’s individual application (horizontal bar plot) and joint application (vertical bar plot to variant calls. Filter combination for each category' of the joint filter plot are shown as dot plot matrix belying their respective column and are sorted in descending order of occurrence. Only the top 40 most frequent filter combinations are shown. The ‘'PASS" filter category denotes variants which passed all filters and were subsequently annotated as somatic variants.

[0039] FIGs. 13A-13B show distribution of somatic variants. FIG. 13A shows allele frequency spectra for all somatic variants aggregated across clones per individual (ind.). FIG. 13B shows count of all variants (light blue) and somatic variants (dark blue) identified or each chromosome.

[0040] FIGs. 14A-14F show telomere gene variants in PTC probands along with telomere length. FIG. 14A shows an exemplary schematic showing interactions of three telomere binding proteins TIN2, TPP1 and POTI (encoded by TINF2, ACD and POTI, respectively). Variants identified in two PTC cohorts are shown above and Cohort 1 variants (n=7 unique) are in bold text. FIG. 14B shows lymphocyte telomere length for the 7 probands from cohort 1, alongside 4 of their first degree relatives who carry the same variants. Each dot represents one individual’s lymphocyte telomere length plotted relative to a clinically used nomogram with percentile lines labeled. Mutant genes are noted and probands are marked by bolded gene name. One 65-year- old TINF2 variant carrier (marked by *) had undergone autologous bone marrow transplantation for multiple myeloma, and her telomere length fell right below the 90th percentile. FIG. 14C shows pedigrees of probands from cohort 1 with each variant shown above. Symbols are defined in key and distinguish individuals with PTC and other malignancies, as well as those for whom DNA sequencing and telomere length were available. Family 3 individual II.2 and Family 6 individuals II.2 and II.4 carry the variant. Family 3 shows anon-carrier w ho developed PTC at age 40. FIG. 14D shows an exemplary schematic of shared POTI variant between Families 2 and 3 in FIG. 14C with flanking shared SNP block from a total 1 Mb queried. FIG. 14E shows chromosomal position of founder ACD missense variant identified in Family 4. The associated haplotype block is shared with two familial melanoma kindreds previously identified in Australia and Denmark. FIG. 14F shows personal and family cancer history of 4 individuals identified in cohort 2 showing multiple malignancies with age at diagnosis noted where available. The variant identified is shown above and key is in the lower comer. Clinical histories for individuals in the pedigrees shown in FIGs. 14C and 14F are further detailed in Table 13.

[0041] FIGs. 15A-15E show clinical characteristics of PTC and co-occurring malignancies. FIG. 15A shows a chart showing germline variant relative to age at last assessment and cancer history in participants identified from cohorts 1 and 2. The 18 individuals in cohort 1 include 7 probands and 11 of their relatives. Variants are shown below with bolded variants denoting the index case from each family. Full squares denote malignant disease and triangles benign neoplasms. *Denotes multifocal or bilateral PTC, and in all cases where no * is shown, multi focal ity status is unknown. The oldest individual died at 86 from Hurthle cell cancer. M denotes males, F denotes females. FIG. 15B shows a table detailing neoplasms depicted in FIG. 15A ordered from benign to malignant by affected tissue. Numbers refer to the number of affected individuals in FIG. 15A (of 22) with the diagnosis. Of note, FIGs. 15A and 15B do not include breast or prostate cancer diagnoses, which were not queried. FIG. 15C shows a swimmers plots showing relative onset of PTC (purple) and melanoma (gold) diagnosis divided by preceding (top panel) or succeeding timeline (bottom). Ages refer to the age at first diagnosis of PTC or melanoma. FIG. 15D shows a computed tomography (CT) image of abdominal desmoid (marked by arrow) seen in an individual with PTC and history of follicular lymphoma. This individual carried a TINF2 mutation and the desmoid tumor was identified incidentally during follow-up lymphoma screening. FIG. 15E shows a CT image of a right kidney tumor in an individual with PTC who developed hematuria. Two synchronous renal cell carcinoma were identified at the time of nephrectomy.

[0042] FIGs. 16A-16I show genetic anticipation in three multi-generation pedigrees. FIG. 16A shows pedigree of parent-offspring pair showing earlier age of onset and increased number of cancer diagnoses in the second generation with variant gene shown above. ‘x2’ refers to two diagnoses with the age at detection noted. FIG. 16B shows ly mphocyte telomere length for individuals in FIG. 16A showing an increased absolute as well as delta telomere length (i.e., difference from the age-adjusted 50th percentile) in the second generation. FIG. 16C shows parentoffspring pair showing earlier age of cancer diagnosis. FIG. 16D shows lymphocyte telomere length for individuals in FIG. 16C showing longer telomere length in the second generation. FIG. 16E shows affected parent-unaffected offspring pair and corresponding telomere length showing longer telomere length. FIG. 16F shows telomere length for individuals who do not carry the variant in the second generation (denoted in gray) falls on the upper end of the normal range in 3 of 4 persons. For FIGs. 16A, 16C, and 16E, the shaded squares indicate individuals with the pathogenic variant shown above. FIGs. 16G-16H show quantitative fluorescence in situ hybridization (FISH) from metaphases derived from peripheral blood mononuclear cells for the parent-child pairs in FIGs. 16C and 16F showing an increase in the average and distribution of the telomere intensities. For each individual, 12-21 metaphases were quantified. FIG. 161 shows somatic co-variant table of PTC cases relative to BRAF Val600Glu mutations and TERT promoter variants. Data on the left are shown for males who were sequenced in The Cancer Genome Atlas (TCGA) database. On the right, summary of somatic variant landscape in 10 PTCs with germline variant status shown below-. Nine of these individuals are male and 1 is female.

[0043] FIG. 17 shows a chart showing genes specifically examined in a solid tumor somatic panel (clinical pipeline). Ages are shown on top and below each name is the germline variant identified. The genes listed to the left have been implicated in telomere maintenance and papillary thyroid cancer biology. TERT promoter mutations were absent as summarized in FIG. 161. Tumor mutation burden estimates are based on analysis of the 400 genes sequenced, and where noted as indeterminate, the quality of the reads fell below the threshold of reliability for assessing single nucleotide somatic variant calls.

[0044] DETAILED DESCRIPTION

[0045] Telomere shortening is a well-characterized cellular aging mechanism, and short telomere syndromes are the most common progeria. Described herein are methods of identifying POTI mutations associated with long telomere length in a subject, wherein the POTI mutations can predispose the subject to a familial clonal hematopoiesis syndrome that is associated with a range of benign and malignant solid neoplasms. In some embodiments, the risk of familial clonal hematopoiesis can be mediated by cellular longevity due to long telomere length as well as a capacity to elongate or maintain telomere length with age.

[0046] Provided herein are methods of identifying a subject as having an increased risk of a cancer or identifying the subject to be in need of cancer screening for early detection or prevention of a cancer, the methods including (a) determining that the subj ect exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (d) stratifying the subject based on the subj ect having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as having an increased risk of the cancer or to be in need of cancer screening.

[0047] Also provided herein are methods of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer, the method including (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) determining that the subject exhibits an increased T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject; (d) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratifying the subject based on the subject having long telomere length, the gene variant, increased TCR or BCR clonality, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject to be in need of cancer screening for early detection or prevention of the cancer. Also provided herein are methods of identifying a subject as a T-cell or bone marrow donor or identifying the subject as a transplant recipient, the method including (a) determining that the subject exhibits a medium telomere length; (b) detecting the absence of a gene variant in the subject; (c) determining that the subject has a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject; (d) detecting the absence of a diseaseindicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratifying the subject based on the subject having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as a T-cell or bone marrow donor or as a transplant recipient.

[0048] Various non-limiting aspects of these methods are described herein, and can be used in any combination without limitation.

[0049] It must be noted that, as used in the specification and the appended claims, the singular forms "a." ‘‘an'’ and “the” include plural referents unless the context clearly dictates otherwise.

[0050] As used herein, the term “about”, when used in reference to a value, refers to a value that is similar, in context to the referenced value. In general, those skilled in the art, familiar with the context, will appreciate the relevant degree of variance encompassed by “about” in that context. For example, in some embodiments, the term “about” may encompass a range of values that are within 25%, 20%, 19%, 18%, 17%. 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%. 6%, 5%, 4%, 3%, 2%. 1%, or less of the referred value.

[0051] As used herein, the term “biological sample” refers to a sample obtained from a subject for analysis using any of a variety of techniques including, but not limited to, biopsy, surgery, and laser capture microscopy (LCM), and generally includes cells and / or other biological material from the subject. A biological sample can be obtained from a eukaryote, such as a patient derived organoid (PDO) or patient derived xenograft (PDX). The biological sample can include organoids, a miniaturized and simplified version of an organ produced in vitro in three dimensions that shows realistic micro-anatomy. Subjects from which biological samples can be obtained can be healthy or asymptomatic individuals, individuals that have or are suspected of having a disease (e.g., cancer) or a pre-disposition to a disease, and / or individuals that are in need of therapy or suspected of needing therapy. In some embodiments, biological samples can include one or more diseased cells. A diseased cell can have altered metabolic properties, gene expression, protein expression, and / or morphologic features. Examples of diseases include inflammatory disorders, metabolic disorders, nervous system disorders, and cancer. The biological sample can include any number of macromolecules, for example, cellular macromolecules and organelles (e.g., mitochondria and nuclei). In some embodiments, the biological sample can be a nucleic acid sample and / or protein sample. In some embodiments, the biological sample can be a carbohydrate sample or a lipid sample. The biological sample can be obtained as a tissue sample, such as a tissue section, biopsy, a core biopsy, needle aspirate, or fine needle aspirate. The sample can be a fluid sample, such as a blood sample, urine sample, or saliva sample. The sample can be a skin sample, a colon sample, a cheek swab, a histology sample, a histopathology sample, a plasma or serum sample, a tumor sample, living cells, cultured cells, a clinical sample such as, for example, whole blood or blood-derived products, blood cells, or cultured tissues or cells, including cell suspensions.

[0052] As used herein, biological samples can include but are not limited to plasma, serum, blood, tissue, tumor sample, stool, sputum, saliva, urine, sweat, tears, ascites, bronchoaveolar lavage, semen, archeologic specimens and forensic samples. In some embodiments, the biological sample is a solid biological sample (e.g., a tumor sample). In some embodiments, the biological sample is a liquid biological sample. Liquid biological samples can include, but are not limited to plasma, serum, blood, sputum, saliva, urine, sweat, tears, ascites, bronchoaveolar lavage, and semen. In some embodiments, the liquid biological sample is cell free or substantially cell free. In some embodiments, the biological sample is a plasma or serum sample. In some embodiments, the liquid biological sample is a whole blood sample. In some embodiments, the liquid biological sample comprises peripheral mononuclear blood cells. In some embodiments, the biological sample is a cerebrospinal fluid (CSF) sample.

[0053] As used herein, the terms “cancer”, “malignancy”, “neoplasm”, “tumor”, and “carcinoma”, refer to cells that exhibit relatively abnormal, uncontrolled, and / or autonomous growth, so that they exhibit an aberrant growth phenotype characterized by a significant loss of control of cell proliferation. In some embodiments, a tumor may be or comprise cells that are precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and / or non-metastatic. The present disclosure specifically identifies certain cancers to which its teachings may be particularly relevant. In some embodiments, a relevant cancer may be characterized by a solid tumor. In some embodiments, a relevant cancer may be characterized by a hematologic tumor. In general, examples of different types of cancers known in the art include, for example, hematopoietic cancers including leukemias, lymphomas (Hodgkin’s and non-Hodgkin’s), myelomas and myeloproliferative disorders; sarcomas, melanomas, adenomas, carcinomas of solid tissue, squamous cell carcinomas of the mouth, throat, larynx, and lung, liver cancer, genitourinary cancers such as prostate, cervical, bladder, uterine, and endometrial cancer and renal cell carcinomas, bone cancer, pancreatic cancer, skin cancer, cutaneous or intraocular melanoma, cancer of the endocrine system, cancer of the thyroid gland, cancer of the parathyroid gland, head and neck cancers, breast cancer, gastro-intestinal cancers and nervous system cancers, benign lesions such as papillomas, and the like.

[0054] As used herein, the term “subject” is intended to refer to any subject. In some embodiments, the subject is cat, a dog, a goat, a human, a non-human primate, a rodent (e.g., a mouse or a rat), a pig, or a sheep. In some embodiments, a subject is suffering from a relevant disease, disorder or condition. In some embodiments, a subject displays one or more symptoms or characteristics of a disease, disorder or condition. In some embodiments, a subject does not display any symptom or characteristic of a disease, disorder, or condition. In some embodiments, a subject is someone with one or more features characteristic of susceptibility to or risk of a disease, disorder, or condition. In some embodiments, a subject is a patient. In some embodiments, a subject is an individual to whom diagnosis and / or therapy is and / or has been administered.

[0055] Telomeres and telomere length

[0056] As used herein, a “telomere” is a region of repetitive nucleotide sequences associated with specialized proteins at the ends of linear chromosomes. Telomeres are a genetic feature most commonly found in eukaryotes, and are known to protect the terminal regions of chromosomal DNA from progressive degradation and ensure the integrity of linear chromosomes by preventing DNA repair systems from mistaking the very' ends of the DNA strand for a double strand break. Telomeres are also critical for maintaining genomic integrity and may be factors for age-related diseases (e g., bone marrow failure, pulmonary fibrosis, immunodeficiency and other related Telomere Syndromes).

[0057] Laboratory studies and clinical observations have shown that telomere shortening and dysfunction is commonly acquired during the process of cellular aging and tumor development, wherein observational studies have found shortened telomeres in many ty pes of experimental cancers (e.g., squamous cell carcinomas of the skin and upper aerodigestive tract, myelodysplasia, and acute myeloid leukemia). Despite having short telomeres, cancer cells ty pically have activated telomerase that will maintain telomeres.

[0058] There is also evidence that mutant genes that may promote telomere lengthening (e.g., POTI, TPP1, RAP1, TERFI, or TERT1 promoter) can cause familial cancer. Such a mechanism contrasts with the TERT loss-of-function mutations seen in the short telomere syndromes that impair the enzyme’s catalytic functions. Somatic TERT promoter mutations are also found in 70% of melanomas as well as a number of other solid tumors. Their high prevalence makes TERT promoter mutations some of the most common somatic mutations seen in human cancer and points to telomerase abundance as critical in cancer initiation and progression.

[0059] Most vertebrate telomeric DNA consists of (TTAGGG)n repeats of variable length, often around 3-20kb. A subtelomere is a segment of DNA between telomeric caps and coding regions of DNA. wherein in vertebrates, each chromosome has two subtelomeres immediately adjacent to the long (TTAGGG)n repeats at each chromosome end. In some embodiments, subtelomeres are considered to be the most distal (farthest from the centromere) region of unique DNA on a chromosome, and they are unusually dynamic and variable mosaics of highly repeated blocks of sequence.

[0060] Telomere length varies greatly between species, from approximately 300 base pairs in yeast to many kilobases in humans, and usually is composed of arrays of guanine-rich, six- to eight-base-pair-long repeats. Eukaryotic telomeres normally terminate with 3’ single-stranded- DNA overhang, which is essential for telomere maintenance and capping. Telomere length also varies within the human population. In some embodiments, in normal human somatic cells, a telomere length can range from about 5 to about 15 (e.g., about 5 to about 14, about 5 to about 12, about 5 to about 10, about 5 to about 8, about 5 to about 7, about 5 to about 6, about 6 to about 15, about 6 to about 14, about 6 to about 12, about 6 to about 10, about 6 to about 8, about 6 to about 7, about 7 to about 15, about 7 to about 14, about 7 to about 12. about 7 to about 10, about 7 to about 8, about 8 to about 15, about 8 to about 14, about 8 to about 12, about 8 to about 10, about 10 to about 15, about 10 to about 14, about 10 to about 12, about 12 to about 15, about 12 to about 14, or about 14 to about 15) kbs.

[0061] Methods of evaluating, telomere length

[0062] In some embodiments, the methods provided herein include analyzing a biological sample from the subject to measure telomere length in a cell from the biological sample. In some embodiments, quantification of telomere length can comprise Flow-FISH. In some embodiments, quantification of telomere length can comprise sequencing. In some embodiments, quantification of telomere length can comprise genome sequencing. In some embodiments, quantification of telomere length can comprise whole genome sequencing. In some embodiment, quantification of telomere length can comprise whole genome sequencing of DNA using various methods (e.g.. short and long read). In some embodiments, quantification of telomere length can comprise terminal restriction fragmentation, quantitative- PCR, or use of fluorescent probes to quantify chromosome-specific telomere lengths. In some embodiments, quantification of telomere length can comprise terminal restriction fragmentation (TRF). In some embodiments, quantification of telomere length can comprise polymerase chain reaction (PCR)-based techniques (e.g., qPCR, MMqPCR, and aTL qPCR). In some embodiments, quantification of telomere length can comprise single telomere length analysis (STELA). In some embodiments, quantification of telomere length can comprise quantitative fluorescence in situ hybridization (Q-FISH). In some embodiments, quantification of telomere length can comprise metaphase chromosome Q-FISH. In some embodiments, quantification of telomere length can comprise interphase Q-FISH. In some embodiments, quantification of telomere length can comprise a primed in situ (PRINS) approach. In some embodiments, quantification of telomere length can comprise a hybridization protection assay (HP A). In some embodiments, quantification of telomere length can comprise single-strand 3’- overhang measurement. In some embodiments, the telomere length is measured by performing qPCR, Flow cytometr -FISH (flowFISH) in leukocyte subsets, or both. In some embodiments, the telomere length is measured by performing whole genome sequencing, flow cytometry, fluorescence in-situ hybridization (flow-FISH), or any combination thereof.

[0063] As described herein, “long telomere syndrome” refers to any of a variety of familial cancer syndromes that are often caused by mutations in telomerase and shelterin genes, wherein these mutations appear to lengthen telomeres and increase the risk of cancer. In some embodiments, a subject having a long telomere syndrome is predisposed to certain cancers, including melanoma and glioma. In some embodiments, telomere-lengthening gene mutations can include a TERT gene mutation, a POTI gene mutation, a TPP1 / ACD gene mutation, a TERFI gene mutation, or a RAP1 / TERF2IP gene mutation. In some embodiments, a POTI gene mutation can comprise Y36N, Y89C, Q94G. Y223C, or R273L. In some embodiments, a POTI gene mutation can comprise L343F, P446Q, P475L, R477T, A532P, I535F, C591W, or Q623H. In some embodiments, a TPP1 gene mutation can comprise V272M and I322F. In some embodiments, a TERT promoter mutation can comprise -124OT, -146OT, — 124 / 125COTT, -138 / 139COTT, -136OT, -1000T, or -57A>C. See Aramburu et al., Comput Struct Biotechnol J. 2020 Jul 3; 18: 1939-1946; Heidenreich et aY. Muiai Res Rev Mutat Res. 2017 Jan-Mar;771 : 15-31, each of which is incorporated by reference in its entirety. Methods of identifying a subject as having an increased risk of a cancer or a need of cancer screening for early detection or prevention of a cancer

[0064] Provided herein are methods of identifying a subject as having an increased risk of a cancer or a need of cancer screening for early detection or prevention of a cancer, the method including (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) detecting a diseaseindicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identify ing the subject as having an increased risk of the cancer.

[0065] As used herein, the term “stratify ’' refers to assigning given subject to a group (e.g., a group that will receive a particular treatment regimen). In some embodiments, a subject can be stratified and placed in a therapy category, wherein a treatment regimen in the therapy category is assigned to the subject. In some embodiments, stratification of a subject can be used in prospective or retrospective clinical studies. In some embodiments, stratification of a subject can be used to assign a prognosis or a prediction regarding survival or chemotherapy or radiotherapy sensitivity. In some embodiments, stratification typically assigns a subject to a group based on a shared mutation pattern, a shared telomere length pattern, a shared diseaseindicative phylogenetic single-colony hematopoietic lineage pattern, and / or other observed characteristic or set of characteristics.

[0066] In some embodiments, stratification of a subject can be based, at least in part, on the subject having a longer telomere length. In some embodiments, the subject can be determined to exhibit a longer telomere length than a reference subject. In some embodiments, stratification of the subject can be based on the presence of a gene variant in the subject.

[0067] As used herein, a “reference subject” can refer to a subject that exhibits normal telomere length. In some embodiments, a normal telomere length can range from about 5 to about 15 (e.g., about 5 to about 14, about 5 to about 12, about 5 to about 10, about 5 to about 8, about 5 to about 7, about 5 to about 6, about 6 to about 15, about 6 to about 14, about 6 to about 12, about 6 to about 10, about 6 to about 8. about 6 to about 7, about 7 to about 15, about 7 to about 14. about 7 to about 12, about 7 to about 10. about 7 to about 8. about 8 to about 15, about 8 to about 14, about 8 to about 12, about 8 to about 10, about 10 to about 15, about 10 to about 14, about 10 to about 12, about 12 to about 15, about 12 to about 14, or about 14 to about 15) kilobases. In some embodiments, the reference subject can refer to a subject that does not have the telomere-lengthening gene variant. In some embodiments, the gene variant can comprise a gene mutation in a gene associated with long telomere length. In some embodiments, the gene associated with long telomere length can comprises a TERT gene, a POTI gene, a TPP1 / ACD gene, or a RAP1 / TERF2IP gene. In some embodiments, the gene variant can comprise a gene mutation in one or more gene(s) that are associated with long telomere length.

[0068] In some embodiments, the reference subject can be an age-adjusted reference subject. In some embodiments, an age-adjusted reference subject can have a normal telomere length that is calculated to eliminate differences in telomere length that result from differences in age distributions. In some embodiments, the long telomere length comprises a telomere length longer than an age-adjusted reference subject. In some embodiments, a longer telomere length can refer to a telomere length that is longer than a normal telomere length in an age-adjusted reference subject. In some embodiments, a normal telomere length can be within a 10thto 90thpercentile relative to an age-adjusted reference subject. In some embodiments, a longer telomere length can be longer than 90thpercentile relative to an age-adjusted reference subject.

[0069] Also provided herein are methods of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer that include (a) determining that the subject exhibits a longer telomere length than a reference subject; (b) detecting the presence of a gene variant in the subject; (c) determining that the subject exhibits an increased T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject; (d) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and (e) stratifying the subject based on the subject having long telomere length, the gene variant, increased TCR or BCR clonality, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject to be in need of cancer screening for early detection or prevention of the cancer.

[0070] In some embodiments, stratification of the subject can be based, at least in part, on a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage being detected in the subject. As used herein, a "‘phylogenetic pattern of a single-colony hematopoietic lineage’7refers to analysis of differentiation in human hematopoietic colonies derived from a single cell. In some embodiments, a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage can comprise assessing the number of independent clonal mutations, the estimated onset of these mutations, mutation burden, mutational signature, and / or overall clonality in the subject. In some embodiments, a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage can comprise a high mutation burden in the subject than a mutation burden of an age matched healthy control. In some embodiments, a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage can comprise a higher number of somatic mutations in the subject than a number of somatic mutations in a healthy control.

[0071] In some embodiments, detecting a disease-indicative phylogenetic pattern of a singlecolony hematopoietic lineage in the subject can comprise genome sequencing. In some embodiments, the reference subject can refer to a subject that does not show a diseaseindicative phylogenetic pattern of a single-colony hematopoietic lineage.

[0072] In some embodiments, methods described herein can further include detecting a gene variant in the subject. In some embodiments, the gene variant comprises a POTI mutation, a TERT promoter mutation, an ACD / TPP1 mutation, a TINF2 mutation, a TERFI mutation, or aRAPl mutation. In some embodiments, thegene variant comprises a loss of function mutation. In some embodiments, the gene variant comprises a gain of function mutation. In some embodiments, the gene variant comprises a gene mutation that is selected from the group consisting of an insertion, a deletion, a substitution, a deletion-insertion, a duplication, an inversion, a frameshift, a repeat expansion, a translocation, and combinations thereof.

[0073] In some embodiments, the cancer comprises a benign solid or hematologic neoplasm. In some embodiments, the cancer comprises melanoma, thyroid cancer, lung cancer, neuronal cancer, glioma, parotid and parathyroid adenoma, angiosarcoma, osteosarcoma, soft tissue sarcoma, bladder cancer, kidney cancer, colorectal cancer, lymphoma, or leukemia. In some embodiments, the cancer is a malignant solid neoplasm. In some embodiments, the cancer comprises gliomas and other neuronal cancers, thyroid cancers, sarcoma, epithelial malignancies, colorectal cancer, renal cell cancer, or lung cancer.

[0074] Methods of identifying a subject as a T-cell or bone marrow donor or a transplant recipient

[0075] Provided herein are methods of identifying a subject as a T-cell or bone marrow donor or a transplant recipient, the method including (a) determining that the subject exhibits a medium telomere length; (b) detecting the absence of a gene variant in the subject; (c) determining that the subject has a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject; (d) detecting the absence of a disease-indicative phylogenetic assessment of a single-colony hematopoietic lineage in the subject; and (e) stratify ing the subject based on the subject having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage.

[0076] As used herein, the term ‘'clonality’’ refers to a uniform population of cells that are derived from a single clone. In some embodiments, a clonal population of cells comprises a population of malignant cells. In some embodiments, TCR or BCR clonality can be assessed through immunophenotypic analysis or assessment of the TCR or BCR repertoire. In some embodiments, TCR or BCR clonality can be represented by a clonality score, wherein the clonality score is between 0 and 1. For example, a clonality score of 0 represents a maximally diverse population with even frequencies, and a score closer to 1 represents a repertoire driven by clonal dominance. In some embodiments, assessment of a subject’s TCR or BCR clonality can be performed by genome sequencing.

[0077] In some embodiments, a medium telomere length can be indicative of a subject being a suitable T-cell or bone marrow donor or a transplant recipient. In some embodiments, the absence of a telomere-lengthening gene variant can be indicative of a subject being a suitable T-cell or bone marrow donor or a transplant recipient. In some embodiments, a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality can be indicative of a subject being a suitable T-cell or bone marrow donor or a transplant recipient. In some embodiments, the absence of a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage can be indicative of a subject being a suitable T-cell or bone marrow donor or a transplant recipient. In some embodiments, the transplant can include a hematopoietic stem cell transplant.

[0078] EXAMPLES

[0079] The disclosure is further described in the following examples, which do not limit the scope of the disclosure described in the claims.

[0080] Telomere length measurement

[0081] Telomere length was measured using flow cytometry and fluorescence in situ hybridization at Johns Hopkins Pathology Laboratories, and the data were plotted relative to a clinically- validated nomogram as described.

[0082] Example 1 - Telomere length and pedigrees of POTI mutation carriers

[0083] 17 POTI heterozygous mutation carriers who were consecutively recruited from five unrelated families (FIGs. 1A-1B) were studied. Telomere length in all 13 assessable living individuals was longer than the 90th percentile, and in 9 of 13, it was longer than the 99th percentile (probability by chance alone is less than 0.001%). Mutation carriers also had longer telomeres than their relatives (mean +3.0 kb vs. +1.2 kb relative to the median for age, P<0.001 Mann- Whitney test, all P-values hereafter adjusted for multiple testing) (FIG. 1A). The POTI variants were absent or rare in population databases (Table 1), although one variant, p.I78T, is a known Ashkenazi founder mutation. POTI p.I78T and p.R273Q fell in conserved telomere DNA-interacting domains, and cells derived from mutation carriers showed they had mildly decreased POTI protein levels as well as defective telomere DNA binding in vitro. The remaining variants disrupted protein and mRNA levels to near half that of controls by altering the reading frame or splicing. These data indicated that POTI haploinsufficiency is associated with long telomere length.

[0084] [Table 1] POTI germline mutations and their prevalence in germline and somatic databases

[0085] Example 2 - Benign and malignant neoplastic manifestations among POTI mutation carriers

[0086] The clinical impact of the POTI variants was assessed and a wide spectrum of neoplasms involving solid and hematopoietic tissues that ranged from benign to malignant (FIGs. 2A-2E) was found. The melanocyte spectrum, for example, ranged from eye melanosis and dysplastic nevi to invasive and metastatic melanoma (FIGs. 2B, 2F). Supporting a melanocyte spectrum phenotype, the six septuagenarians queried reported delayed hair graying (Table 2). Goiters and papillary thyroid cancer were also documented with benign thyroid disease being more common (FIGs. 2A, 2C). There was also a high prevalence of mesenchymal neoplasms ranging from uterine fibroids to soft tissue sarcomas and mesenteric desmoid (FIGs. 2A, 2F). Epithelial derived cancers included renal cell, urothelial and colorectal carcinoma (FIGs. 2D-2F). In nearly all the cases, tumors were resectable, but the most lethal neoplasm was malignant glioma, which affected 2 of 17 individuals (12%) (FIGs. 2A, 2E-2F). There was additionally a range of B cell neoplasms including B cell lymphocytosis of uncertain significance, pediatric acute lymphocytic leukemia and adult small lymphocytic lymphoma (FIG. 2F). A parallel pattern for T cells was documented with diagnoses ranging from cutaneous T cell lymphoma to large T cell lymphoma (FIG. 2F), and multiple lymphoid neoplasms co-occurred in the same individuals at times, along with solid tumors (FIG. 2A). In an individual with cutaneous T cell lymphoma, fatal large T cell lymphoma involving the bowel developed after treatment with immune checkpoint inhibitors for metastatic melanoma (FIG. 3A). There was also evidence of myeloid malignancy with fatal treatment-related acute myeloid leukemia following alkylating therapy for glioma, and another individual had an evolving myeloproliferative neoplasm (FIG. 2A).

[0087] [Table 2] Onset of hair graying in adults with POTI mutations

[0088] Example 3 - Lymphoid and myeloid clonal hematopoiesis predisposition among POTI mutation carriers A

[0089] The lymphoid and myeloid neoplasm aggregation led to a hypothesis that longer telomere length may provide a selective advantage that supports clonal evolution. This hypothesis seemed plausible because there was no DNA damage sensitivity in cells derived from POTI mutation carriers. First, the diversity of peripheral T cells was assessed by sequencing the T cell receptor (TCR)-VP variable complementarity determining region (CDR3) and found three of 12 individuals had productive clonality scores near the 99th percentile relative to healthy age-matched controls (FIG. 3B), and the top 10 most frequent clones were over-represented in the repertoire. In a second group of unrelated POTI mutation carriers, two of six individuals also fell near the age-adjusted 99th percentile (FIG. 3B). Cytomegalovirus (CMV) exposure is the primary driver of T cell clonality with aging, but the increased clonality rate was evident irrespective of whether these POTI mutation carriers were compared to CMV positive or negative controls (FIG. 3B). In total, 5 of 18 POTI mutation carriers (28%) from five unrelated families with distinct POTI mutations, had evidence of increased clonality including 4 individuals with no prior history of hematologic malignancy (probability by chance alone less than 0.01%). Assessment by flow cytometry confirmed the lymphoid clonal predisposition, as nearly all 5 POTI mutation carriers had abnormal T cell populations in addition to incidentally identified B cell clones.

[0090] Next, the prevalence of clonal mutations was examined, adopting standard clinical criteria for clonal hematopoiesis of indeterminate potential (CHIP) [i.e., mutations with >2% variant allele frequency (VAF)]. Sixty-seven percent of POTI mutation carriers had CHIP compared to 10% of their relatives [8 of 12 vs. 2 of 21, P=0.001, Odds ratio 18.9 (95% confidence interv al 2.88-124.2), Fisher’s exact test, FIG. 3C], Clone sizes were also larger (6 of 17 with VAF >10%), whereas in non-carriers, the mutations fell in non-canonical CHIP genes (Table 3) and were small (VAF < 5%). DNMT3A was the most commonly mutated gene, followed by the JAK2V617F hotspot. Several individuals had multiple CHIP mutations and three septuagenarians had multiple DNMT3A clones >1% VAF (FIG. 3D). In a second group of POTI mutation carriers, all three individuals over age 65 had CHIP, revealing significantly higher penetrance among older POTI mutation carriers even when compared to an older group of controls [8 of 8 POTI mutation carriers >65 years, median age 69.5 vs. 13 of 30 controls >70 years, median age 74.5, P=0.005, Fisher’s exact test] (Table 4) and despite a lower depth of coverage in the former group (600x for POTI mutation carriers vs. 1300x for controls). Ultra-deep sequencing further uncovered a greater proportion of POTI mutation carriers with JAK2V617F clones [7 of 12 vs. 1 of 23, P=0.002, Odds Ratio 12.8 (95% confidence interval 2.8-148.9), FIG. 3E], Genome-wide association studies have identified an intronic ‘GGCC’ haplotype within JAK2, which increases the risk of JAK2-associated disease by 4-fold, and in two of the families. ‘GGCC’ heterozygous or homozygous haplotypes were present (FIG. 3C). However, the ‘GGCC’ genotype alone did not explain the difference in JAK2 clonal hematopoiesis as the haplotype was equally distributed among carriers and noncarriers, but POTI mutation carriers showed an increased risk [6 of 10 ‘GGCC’ positive POTI mutation carriers had JAK2V617F vs. 0 of 15 ‘GGCC’ positive non-carriers, P=0.001, Odds Ratio 18.0 (95% confidence interv al 2.1-956.8)].

[0091] The lineage of JAK2V617Fwas queried in leukocyte fractions and confirmed they were myeloid derived and expanded longitudinally with age. However, DNMT3A mutations, in some cases, were shared across myeloid and lymphoid lineages suggesting they arose in a primitive progenitor. Phylogenetic inference of single cell hematopoietic progenitors (using whole genome sequencing) estimated the onset of driver CHIP mutations to be early in life (e.g.. prior to age 4 in one 74-year-old mutation earner), supporting the conclusion that the driver-carrying lineages are long-lived vnPOTl mutation carriers. Supporting their longevity, progenitors from POTI mutation carriers had higher somatic mutation burdens (FIGs. 3G-3H vs. 3F), and the topologies of phylogenetic trees also showed evidence of oligoclonality relative to chronological age. For example, the tree from a 48-year-old exhibited two independent JAK2V617F clonal events and showed phylogenetic evidence of greater oligoclonality compared to a related individual who was two decades older (FIGs. 3G vs. 3F), and similar to trees of controls in their eighth decade. Another POTI mutation carrier carried five identifiable DNMT3A somatic mutations (biallelic mutations defining a large clade as well as three other mutations including one identified by bulk sequencing, FIG. 3H). Consistent with an extended replicative history for these clones, the single base substitution (SBS) signatures were dominated by C>T transitions and explained by clock-like age-associated patterns (COSMIC SBS1 and SBS528).

[0092] [Table 3A] Somatic variants identified in 12 POTI mutation carriers (n=17 variants)

[0093] [Table 3B] Somatic variants identified in 21 POTI non-carrier relatives (n=3) [Table 4] Somatic variants in 30 controls over age 70 (n=22 variants)

[0094] Example 4 - Genetic anticipation and longitudinal telomere length changes among POTI mutation carriers

[0095] It was next queried whether the clonal predisposition was due to the long telomere length or a telomere lengthening advantage and evidence was found supporting both mechanisms. A role for long telomere length was apparent from the genetic anticipation, wherein offspring of POTI mutation carriers, who carried the same germline mutation, developed cancer several decades earlier (and in some cases developed larger somatic clones at younger ages, FIG. 4A). It was surmised that this genetic anticipation is due to inheritance of long telomeres, as has been documented for the short telomere syndromes, although a telomere length difference was not readily detected across consecutive generations (it was noted that a difference was detected across three generations in other mutant POTI families). The genetic anticipation led to ahypothesis that / V / / 7 mutations promote telomere lengthening with aging. Telomere length was measured longitudinally, two years after initial study enrollment, and it was found that the rate of shortening was slower among POTI mutation carriers compared to controls, while their relatives showed similar shortening (FIGs. 4B-4C). There was also an absolute gain in telomere length among POTI mutation carriers in lymphocytes and granulocytes compared to their non-carrier children and siblings (mean +0. 17 kb vs. -0.47 kb in lymphocytes, and +0.22 kb vs. -0.41 kb in granulocytes, P=0.02 and 0.04. Mann- Whitney test, respectively, FIG. 4D).

[0096] To understand the mechanism that underlies the high rate of CHIP among POTI mutation carriers with aging, the effect of the inherited capacity to lengthen telomeres and long telomere length (99th percentile) (such as with POTI mutation carriers) on the clone size of a single somatic driver mutation that arose in a hematopoietic progenitor at birth over a 90-year lifespan was simulated. The rates of CHIP to POTI wildtype individuals were compared with long, average and short telomere length who acquired normal telomere shortening (respectively 99th, 50th and 1stpercentiles as defined in clinically-validated nomograms). Based on 10,000 replicate simulations per group, POTI mutation carriers with long telomere length exhibited the largest clone sizes (FIG. 4E). Long telomere length, in the 99th percentile group also supported clonal longevity' with age, although to a lesser extent (FIG. 4E). By contrast, clones initially grew in the shortest telomere length group but subsequently vanished because of dropout at the threshold of telomere-induced senescence (FIG. 4E). Drawing from these data, the proportion of individuals with CHIP was highest among POTI mutation carriers with long telomere length, followed by individuals with long telomere length alone.

[0097] Example 5 - Phylogenetic Studies

[0098] The lineage of JAK2 V617F in leukocyte fractions were queried and confirmed that they were myeloid-derived and that they expanded longitudinally7with age. However, in some cases, DNMT3 A mutations were shared across myeloid and lymphoid lineages, which supports their having arisen in a primitive progenitor. Phylogenetic inference of colonies derived from single-cell hematopoietic progenitors (with the use of whole-genome sequencing) in two related POTI mutation carriers yielded results consistent with driver CHIP mutations arising early in life (e.g., before 4 years of age in one 74-year-old carrier), supporting the hypothesis that driver-carrying lineages are long-lived in POTI mutation carriers (FIGs. 5A-5D). Moreover, progenitors from POTI mutation carriers had higher somatic-mutation burdens than a related noncarrier, and the topologies of phylogenetic trees also showed evidence of oligoclonality relative to chronologic age. For example, the phylogenetic tree of a 48-year-old carrier showed two independent JAK2 V617F clonal events and phylogenetic evidence of oligoclonality that was greater than that in a relative who was 18 years older and similar to that in controls in their eighth decade of life. The other POTI mutation carrier carried five identifiable DNMT3A somatic mutations (biallelic mutations defining a large clade, as well as three other mutations, including one identified by bulk sequencing) (FIGs. 6A-6C). A dominance of C^T transitions among the single-base substitution signatures and clocklike, age-associated patterns are consistent with an extended replicative history for these clones (FIG. 7)

[0099] Example 6 - Mechanisms Underlying Clonal Predisposition

[0100] It was asked whether the clonal predisposition was due to the long telomere length or a telomere-lengthening advantage and found evidence supporting both mechanisms. A role for long telomere length was suggested by the appearance of genetic anticipation, wherein offspring of POTI mutation carriers who carried the same germline mutation had cancer that developed several decades earlier than that in their parents (and in some cases, larger somatic clones developed at younger ages). It was proposed that this genetic anticipation is due to inheritance of long telomeres, as has been documented for the short telomere syndromes, although a difference in telomere length across consecutive generations in the families was not detected. The apparent genetic anticipation led to hypothesizing that POTI mutations promote telomere lengthening with age, so telomere length was measured 2 years after study enrollment. The data support a slower rate of shortening among POTI mutation carriers than among controls, whereas their noncarrier relatives had a rate of shortening similar to that among controls. Telomeres of lymphocytes and granulocytes in POTI mutation carriers showed an absolute gain in length as compared with that of their noncarrier children and siblings (mean change, 0.17 kb vs. -0.47 kb in lymphocytes and 0.22 kb vs. -0.41 kb in granulocytes).

[0101] To understand the mechanism that underlies the high incidence of CHIP among POTI mutation earners with aging, the effect of the inherited capacity to lengthen telomeres (such as with POTI mutation carriers) and long telomere length (99th percentile) (such as with POTI mutation carriers) was simulated, both independently and together, on the clone size of a single somatic driver mutation (such as the JAK2 V617F hotspot variant) that arose in a hematopoietic progenitor at birth over a 90-y ear lifespan. The effects of intermediate and short telomere length were also simulated, at the 50th and 1st percentiles, respectively; percentiles were previously defined in clinically validated nomograms. On the basis of 10,000 replicate simulations per group, POTI mutation carriers with long telomere length had the largest clone sizes (FIGs. 8A-8B). In this simulation, long telomere length also supported clonal longevity with age, although to a lesser extent. By contrast, simulated clones with short telomeres initially expanded but then vanished because of dropout at the threshold of telomere-induced senescence (FIG. 9). These data support a model in which the proportion of persons with CHIP was the highest among POTI mutation carriers with long telomere length, followed by persons with long telomere length alone (FIG. 10).

[0102] Germline genotyping

[0103] POTI variants were genotyped from DNA isolated from peripheral blood by PCR amplification and Sanger sequencing. For deceased individuals, sequencing was performed on archived formalin fixed paraffin embedded tissue. The JAK2 GGCC' haplotype block was inferred by sequencing rs!2343867, which is in linkage disequilibrium with three risk polymorphisms. Primers are listed in Table 5. Variants were deposited in the ClinVar Annotation Database.

[0104] Radi osensi ti vi ty

[0105] Lymphoblastoid cell lines (LCLs) were derived from peripheral blood mononuclear cells by Epstein Barr virus immortalization. Sensitivity to ionizing radiation was assayed by irradiating 3000 LCLs / well in four replicates using a Gammacell 40 Extractor, and cells were counted at 7 days (Cell Counting Kit-8, Dojindo Molecular Technologies) to assess viability.

[0106] [Table 5] Primer sequences

[0107] POTI immunoblot and quantitative real time PCR

[0108] LCLs were lysed in RIPA lysis buffer (Cell Signaling Technology) supplemented with protease inhibitor (Roche) and benzonase nuclease (Millipore). Protein was resolved on Bolt 8% BisTris gels with MOPS-SDS buffer and transferred to Poly vinylidene difluoride membrane using the iBlot 2 Dry Blotting System (Thermo Fisher Scientific). Membranes were pre-treated with SuperSignal Western Blot Enhancer then blocked with Intercept blocking buffer (LI-COR). In vitro transcribed POTI (NM_015450) was generated. Primary antibodies details are as follows: POTI (rabbit, NB500-176. 1:500, Novus). Myc (mouse, clone 4A6. 1: 1000, Millipore), and B- tubulin (rabbit, ab6046, 1 : 100,000, Abeam). POTI secondary antibody was conjugated to horseradish peroxidase (HRP) (Goat, 7074, 1 : 10,000; Cell Signaling Technology), and blots were visualized by chemiluminescence (SuperSignal West Pico PLUS, Thermo Scientific). Other secondary antibodies were conjugated to IR680 or IR800 (donkey, 1 : 10,000, LI-COR) and blots were visualized by LICOR’s Odyssey scanner and quantified. POTI cDNA levels were measured by quantitative reverse transcription PCR using the SYBR green method (primers listed in Table 5).

[0109] POTI functional studies

[0110] The impact of POTI missense mutations on telomere DNA binding was tested in a gel shift assay. To assess the effect of splice junction variants, LCLs were incubated with cycloheximide diluted in dimethyl sulfoxide-containing medium (50 pg / mL, Cell Signaling). cDNA was amplified using exon-spanning primers and products were gel isolated for Sanger sequencing (primers listed in Table 5).

[0111] T cell receptor (TCR) diversity

[0112] TCR-VB Complementarity Determining Region 3 (CDR3) repertoire diversity was examined using the immunoSEQ Survey Assay (TCRv4b, Adaptive Biotechnologies), and clonality metrics were obtained using immunoSEQ Analyzer 3.0. TCR clonality was plotted relative to data for healthy bone marrow donors with known cytomegalovirus serostatus.

[0113] Immunophenotyping was performed on ficoll-separated peripheral blood mononuclear cells at the Johns Hopkins Flow Cytometry Laboratory. Cell suspensions were incubated with monoclonal antibody combinations (BD Biosciences) that were used at concentrations titrated for optimal staining. The panel included antibodies for CD3, CD19, CD200, CD56, CD45, CD10, CD20, surface kappa and lambda light chains, and CD38. Some specimens were subjected to extended antibody panels, which included polyvalent kappa and lambda light chains, CD2. CD7, CD4, CD34, CD5, CD8, CD27, and CD28. An antibody directed at the TCR-P chain constant region 1 was added to identify isotypes (C0 TCR; clone JOVI-1, BD Biosciences). Specimens were analyzed on the BD FACSCanto 10-color system (BD Biosciences). List mode data files were acquired and analyzed for each specimen using FACSDiva (BD Biosciences) and Infinicyt (Cytognos), respectively. An antigen was considered positive if the cell population of interest show ed a homogeneous distribution with the median intensity at least 2 log channels above that seen in the control, or if there was a heterogeneous distribution of antigen expression, such that a subpopulation of cells w as above that seen in the control.

[0114] Clonal hematopoiesis next-generation sequencing from bulk DNA

[0115] Somatic variant sequencing was performed using a customized clinical pipeline at the Johns Hopkins Pathology Laboratories. The panel included 87 genes implicated in hematologic malignancies (Table 6). Libraries were prepared using xGen Dual Index UMI adaptors and hybridized to an IDT DNA probe set (Integrated DNA Technologies). Final libraries were generated using IDT xGen Hybridization blockers and Wash Kit and treated with blocking reagent (Illumina Free) to reduce sequencing aberrancies. Sequencing was performed on NovaSeq 6000 (SI Reagent Kit vl, 200 cycles, Illumina). Mean deduplicated consensus coverage was 612x (range 326-1151) with 96% on target at 15 Ox or greater depth. Reads w ere aligned to GRCh37 using BWAvO.7.17. Variants were called and annotated using a custom pipeline, cross referenced with HaplotypeCaller (GATK 3.3), and annotated with ANNOVAR (v 10122020) 13. Data from septuagenarian controls were from the Women’s Health and Aging Study (WHAS), and the sequencing and analysis of variants were performed using the same clinical pipeline as for the remaining samples, except the depth of coverage was two-fold higher (mean 1336x).

[0116] Somatic mutation analysis Non-synonymous and canonical splicing variants with variant allele frequency (VAF) greater than or equal to 0.01 and minimum 50x coverage were retained. Variants with VAF of either 0.35-0.65 (heterozygous calls) or >0.95 (homozygous calls) were excluded as likely germline polymorphisms. Recurrent technical artifacts were excluded if they were present in clinical validation samples that were sequenced on the same platform. Filtered variants were manually inspected in Integrated Genome Viewer (IGV), and included if they were present on at least 5 unique molecular bar codes (MBC) with all reads for each MBC containing the variant. Variants that had minor allele frequency >0.001 in gnomAD v2.2.1 were also excluded as potentially germline. [Table 6] DNMT3A and JAK2V617F targeted genotyping and droplet digital PCR ( dPCR)

[0117] To assess for low frequency somatic JAK2V617F mutations, targeted deep sequencing (mean coverage 5100x, range 2800-10115) was performed using the Ion AmpliSeq Cancer HotSpot Panel v2 (Life Technologies). Amplified products were sequenced on Ion S5 XL (Thermo Fisher). To test the lineage of origin, droplet digital PCR (ddPCR) was performed on DNA isolated from leukocyte fractions (EasySep Human Myeloid Positive Selection and Human T cell Enrichment kits, Stem Cell Technologies). Primers are listed in Table 5. Somatic DNMT3A (NM_022552) strand phasing was performed by cloning and sequencing products of cDNA amplification. cDNA was derived from LCLs and the amplicon encompassed the two DNMT3A somatic mutations (550 base pairs apart).

[0118] Whole genome sequencing of hematopoietic colonies

[0119] Ficoll-separated peripheral blood mononuclear cells were plated at a concentration of 2 x 105cells per 3 cm tissue culture dish in Methocult media following manufacturer's instructions (Stem Cell Technologies, Vancouver) and incubated at 37°C. Single erythroid colonies were plucked on day 14 after seeding; these erythroid were chosen for their larger size and higher DNA content. DNA was extracted using MagMAX DNA Multi-Sample Ultra kit (Applied Biosystems). Libraries were prepared from 10 ng of DNA derived from each single colony (sample extracted from whole blood was used as a ‘bulk’ sample) using the Qiagen DNA FX library preparation kit with unique dual indexes (UDI). Fragmentation was run for 16 minutes with no enhancer. Adapters were diluted 1 : 10 and samples were PCR amplified for 10 cycles. Paired-end whole genome sequencing was performed (150 bp) at 15x targeted depth of coverage on an Illumina Nov aSeq vl.5 kits (S4 and S2, 300 cycle). 50 colonies were sequenced in addition to the bulk DNA sample for 3 individuals. A single colony sample failed to sequence and was removed from subsequent analyses. Raw sequencing reads are under controlled access and available via the NCBI’s dbGaP accession phs003207.vl.pl.

[0120] Read alignment

[0121] Raw reads for the whole genome sequencing data were aligned to the GRCh38 human reference genome assembly (GRCh38.dl . vdl ; obtained from the NIH National Cancer Institute Genomic Data Commons) using bwa-mem. File conversion, indexing, sorting, and removal of PCR duplicates were performed with the SAMtools suite. Read alignment statistics are included in FIG. 11 A.

[0122] Variant calling

[0123] As each sequenced clone was experimentally designed to represent a homogenous population of cells derived from a single hematopoietic progenitor; as such, genotyping was performed using the GATKHaplotypeCaller algorithm to produce gVCF files for each clone. Variant calls were merged across all clonal samples using the GATK CombineGVCFs command, and joint genotyping was performed using the GenotypeGVCFs command to produce variant call files composed of both germline and somatic mutations for every clone from a given donor. Bulk peripheral blood samples (henceforth, “germline matched reference”) were also produced and analyzed independently of clone samples. Aggregated variant calls for all the whole genome sequence data are shown in FIG. 11B.

[0124] Variant call filtering

[0125] A stringent variant filtering strategy was developed with the goal of isolating and retaining high-confidence somatic mutations while removing germline variation and spurious technical artifacts. This somatic mutation filtering heuristic was modified to leverage the clonal sequencing protocol described herein. Each filter is described below:

[0126] • VQSR: The GATK VariantQualityScoreRecalibrator (VQSR) was applied to filter sites for technical artifacts which could be inferred from base quality and read-mapping statistics, according to GATK best practices with the following modifications tailored to the somatic clone sequencing scenario: 1) Excess heterozygosity was excluded from the metrics under consideration, as this measurement relies on assumptions of Hardy- Weinberg Equilibrium, which are violated by the study design; and 2) 'BaseQRankSum' (z-score from Wilcoxon rank sum test of alternate vs. reference base qualities) was added as a metric under consideration.

[0127] • GermlineHet: Heterozygous sites present in each individual’s matched germline reference (which met the AlleleBalance filter of 0.25) were removed.

[0128] • CommonPopVariant: Common germline polymorphisms observed in the population (allele frequency > 0.01) were removed using the gnomAD v3.1.219 variant database as reference.

[0129] • MissingData: Removed sites where over 20% of samples lacked genotypes. • UnreliableSlle: The 1000 Genomes Project genome accessibility mask20 was used to remove variants in regions of low technical accessibility.

[0130] • MultiAllelic: Multiallelic variants were removed.

[0131] • HomAlt: Variant sites with an allele frequency of 1.00 were removed.

[0132] • HighHet: Variants where over 80% of genotyped samples were called as heterozygous were removed.

[0133] • Cluster edEvents: Variants within 10 bp of any other variant were removed.

[0134] • Two depth filters were applied: o Depth ilter: Removed sites where >80% of heterozygous samples possessed sequence depths below the 10th percentile of coverage, given the mean sequencing depth of clones from that donor and assuming a Poisson distribution. o AlleleBalance: An allele balance filter to remove sites where fewer than half of heterozygous samples (which met the depth filter) had an allele balance of at least 1 / 3.

[0135] FIGs. 12A-12C show- the respective variant filtering statistics for each individual studied. Postfiltering summaries of somatic variant distributions are included in FIGs. 13A-13B. All somatic variants per clone were annotated using GATK’s Funcotator tool (FIG. 7).

[0136] Phylogenetics

[0137] SNP sites which passed all filtering steps were used to infer the phylogenetic relationships of the clones with maximum-likelihood using IQTree v.2.2.021. The maximum-likelihood topology was inferred with the best-fit substitution model using ModelFinder22, and node support values were estimated using 10,000 ultrafast bootstrap replicates with nearest neighbor interchange to avoid overestimating branch lengths due to severe model violations. All nodes under 95% ultrafast bootstrap support were collapsed.

[0138] Using the topologies generated above, branch lengths were transformed to reflect the acquisition and accumulation of somatic SNPs using the maximum-likelihood algorithm implemented in ‘treemuf . Ultrametric branch length adjustments to visualize time-dependent somatic SNPs acquisition (FIGs. 5A-5D) were estimated using the "iterated rew eighted means approach’. Unedited tree files with node supports presented as "[SH-like approximate likelihood ratio test value] / [ultrafast bootstrap support percentage]’ and branch length estimations measured in substitutions per site are available on GitHub performed using ape (version 5.6-2)24, and TreeTools (version 1.8.0) libraries for R (version 4.2.0). Mutation signature assignment

[0139] SigProfilerAssignment was used to assign single-base substitution (SBS) signatures from The Catalog of Somatic Mutation in Cancer (COSMIC) to the collection of somatic mutations per clone, independently. Each inferred SBS profile was then reconciled with all other samples from the respective individual and visualized as SBS signature count distributions (FIG. 7).

[0140] In silico simulation

[0141] Simulations were used to test the role of telomere length (TL) on the longevity of a single heterozygous gain-of-function somatic mutation arising in one of 100,000 hematopoietic stem cells at birth. The simulation was run for 32.850 days, reflecting a 90-year lifespan, and for each condition, 10,000 simulations were run to capture variability. Four TL backgrounds were simulated, each representing the value at a certain percentile as defined by flowFISH in the human population. These TL backgrounds were at the 1st (8.6 kb), 50th (11.0 kb), and 99th (13.4 kb) population percentiles, as well as a POTI mutant background with 13.4 kb TL. To reflect heterogeneity within the stem cell population’s TL at birth, a TL standard deviation of 1.25 kb was applied, as estimated by single colony TL measurements by whole genome sequencing. A 100 bp / division telomere shortening rate in hematopoietic cells was utilized, and 50 bp / division gain in the POTI mutant group was estimated based on the data (FIG. 8B). The simulation was constructed using the software SLiM26. Within each day of the simulation, each cell underw ent one of two fate decisions: division or non-division, where division could be asymmetric (producing a differentiated daughter cell), symmetric (creating an additional progenitor), or differentiation (dropout from the progenitor population). Cells also acquired a TL change after each division. This introduces an element of stochastic drift, whereby cells carrying a driver mutation may drop out or never divide regardless of their fitness advantage. All cells also acquired a TL change after each division, drawn from a normal distribution to further incorporate variability in TL distributions. Finally, every7cell with TL <4 kb experienced cell death w ith a probability dependent entirety on TL, with likelihood of death increasing linearly from 0 (certain survival) to 1 (certain death) between 4 kb and 2 kb.

[0142] Analysis plan

[0143] Data were analyzed for available numbers (sample of convenience) without a priori sample size calculations. The first primary analysis objective was to compare the incidence of clonal hematopoiesis of indeterminate potential (CHIP) in POTI mutation carriers and their relatives with Fisher’s exact test. A two-sided P-value <0.05 was considered statistically significant for this analysis. The second primary analysis objective was to compare TL in POTI mutation carriers relative to the expected number based on the validated nomogram of controls. Specifically, the probability of observing the number of POTI mutation carriers who fell in the >90th and >99th percentile ranges relative to the expected proportions of 10% and 1% was calculated, respectively, using the binomial distribution. Secondary analyses compared the age- adjusted telomere length in POTI mutation carriers and their relatives who did not carry a POTI mutation. This analysis was performed using the deltaTL in each individual (difference in length from 50th percentile for age), and the means for each group were then compared among the two groups. Exploratory analyses included: 1) Estimating the rate of detectable JAK2V617F clones >0.1% among related ‘GGCC’ haplotype carriers with and without POTI mutations with an odds ratio and 95% confidence interval adjusted for small sample size using the ‘epitab’ package in R31; and 2) Comparing the change in telomere length over time in POTI mutation carriers and non-carriers. The latter compared both the deltaTL across two timepoints as well as the absolute change in both lymphocytes and granulocytes. P-values were included for the only primary hypotheses which were determined post-hoc. Data were analyzed in R version 4.2.1 and graphs were generated using GraphPad Prism v8.3.0.

[0144] Subjects

[0145] The prevalence of germline variants were studied in five candidate telomere genes in two Ohio State University (OSU)-based cohorts recruited 1999-2019. Cohort 1 was selected from subjects recruited to the OSU Endocrine Neoplasia Repository with a few participants from a familial PTC Study. To test the hypothesis that germline variants in telomere maintenance genes are enriched in individuals with inherited predisposition and long telomere syndrome features, the first 200 participants who fulfilled these criteria were selected in order of priority: 1) PTC subjects with history of “melanoma” or “nevi” 2) PTC co-occurring with “leukemia” or “lymphoma”, 3) familial PTC (defined as 3 or more affected individuals), 4) Males with PTC diagnosed prior to age 40, and 5) participants with multifocal PTC. Table 7 summarizes the clinical characteristics. Cohort 2 was queried as validation of the three telomere genes identified in cohort 1. It was comprised of 270 unselected PTC subjects who had available blood-derived exome data. Cohort 2 clinical characteristics are shown in Table 8. The study was approved by the Ohio State University and Johns Hopkins Medicine Institutional Review Boards, and all the subjects gave written informed consent. [Table 7] Clinical characteristics of Cohort 1 subjects selected from the Ohio State University

[0146] Endocrine Neoplasia Repository and Familial Papillary Thyroid Cancer (PTC) Study

[0147] (recruited 1999-2019)

[0148] [Table 8] Clinical characteristics of consecutively recruited subjects with papillary thyroid cancer (PTC) with existing exome data (Cohort 2. recruited 2013-2019) Targeted germline sequencing, (Cohort 1)

[0149] Genomic DNA was extracted from peripheral blood using standard methods. For cohort 1, a customized panel targeting the five 32 candidate telomere genes was designed (Twist): TERT promoter (chr5: 1,295, 162-1,295,900 [MIMT872701]), ACD (encodes TPP1, NM_001082486, long isoform [MIM:609377]), POTI (NM_015450), TERF2IP (NM_018975 [M1M:6O5O61]), and T ' INT 2 (NM_001099274). Libraries were prepared from

[0150] 50 ng of genomic DNA, and paired-end sequencing was performed on MiSeq v2 (250 bp). Reads were aligned to GRCh37 with decoy using bwa mem (v0.7. 15). Mean targeted coverage was 95x (range 57-154). As the TERT promoter coverage 1 was poor in the next generation sequencing data, PCR amplification and Sanger sequencing were performed separately using published primers, and sequences were analyzed in SnapGene. Variant calling for the next generation sequencing data was performed with GATK (v3.7). Annotation and filtering was performed using bcftools and ANNOY AR (v4162018). Candidate variants were verified in the proband and related family members who were subsequently recruited by PCR amplification and Sanger sequencing.

[0151] Exome sequencing, analysis

[0152] For cohort 2, candidate genes were analyzed in existing exome data that were generated on the Nimblegen v3 platform using Illumina HiSeq4000. Target coverage was lOOx and a minimum of 50x was required to call variants. Germline variants were called using Haplotyper algorithm with the Sentieon App [sentieon release 201911 (www.sentieon.com)]. Annotation was performed using Ensembl Variant Effect Predictor, and variants were filtered to have variant allele frequency (VAF) of 0.3 with PHRED QUAL over 100. Candidate variants were verified manually in the Integrated Genome Viewer (v2.13.0) and prioritized as outlined below. Variant annotation conformed to Mutalyzer v3.0.718.

[0153] Variant priori tization

[0154] For cohorts 1 and 2, rare nonsynonymous or splice variants (defined as minor allele frequency (MAF) <0.0001 in gnomAD v2. 1. 1) were considered further if they were nonsense type variants, had Combined Annotation Dependent Depletion (CADD) (vl .6) score >20, or were previously reported in families with cancer in the literature. In cohort 1 , probands and family members were recruited and telomere length greater than the 90th age-adjusted percentile by flow cytometry and fluorescence in situ hybridization (flowFISH) was used for inclusion in the final analysis. Published evidence supporting pathogenicity including prior report of ultra-long telomere length (greater than the 99th percentile by flowFISH) was also considered. Rare candidate variants that did not fulfill these criteria were considered as variants of uncertain significance (Tables 9 and 10 for cohorts 1 and 2, respectively). [Table 9] Rare variants of uncertain significance identified in 5 candidate telomere genes

[0155] (Cohort 1) [Table 10] Variants of uncertain significance identified in three telomere genes (Cohort 2)

[0156] Founder variant analysis

[0157] To assess relatedness among individuals with rare shared variants, we examined SNP array data [Omni-1 Quad-bead chip 1.1M (Illumina)] was examined. Unimputed genotypes were phased using population data using SHAPEFIT (v2.790) with 1000 Genomes Phase 3 reference data (October 2014 release). For one known ACD founder familial melanoma variant, the flanking SNPs were compared to the haplotype block reported in Aoude et al. Relatedness between the two probands sharing a POTI variant was analyzed by examining 1 Mb flanking regions.

[0158] Telomere length studies

[0159] Average telomere length - Peripheral blood average telomere length was measured by flowFISH on cryopreserved ficoll-separated cells at the Johns Hopkins Pathology’ Laboratory’. Each individual’s average telomere length was plotted relative to a clinically-validated age- adjusted nomogram.

[0160] Metaphase telomere length measurement - To assess the telomere length distribution across generations, quantitative telomere FISH (Q-FISH) was performed on metaphases generated from in vitro phytohemagglutinin (PHA)-stimulated cryopreserved peripheral blood isolated cells at Johns Hopkins Cytogenetics Laboratory. FISH was performed, with images captured on a Nikon ECLIPSE Ni yvith Intensilight C-HGFI illuminaotor and fluorescence excitation / emission filters with lOOx oil objective. DAPI and TRITC images captured at fixed exposure times were quantified using Telometer an ImageJ plugin.

[0161] Tumor Sequencing

[0162] The somatic landscape of tumors yvas examined using a targeted panel that included 439 genes implicated in solid malignancies including the TERT promoter. Library preparation and sequencing were performed in the Johns Hopkins Genomics Lab (in two batches in 2021 and 2023). Prior to library preparation, samples were treated yvith NEBNext FFPE DNA repair mix (New England BioLabs) to minimize formalin-induced DNA damage. Libraries were sequenced on Illumina NovaSeq using NovaSeq 6000 SI Reagent Kit vl.5. Mean depth of coverage was 473x (range 128-956) and mean on-target with >150x coverage yvas 77%. FASTQ files were aligned to hgl9 (GRCh37) with Burrows-Wheeler Aligner v0.7. 17. Variants were called using an in-house caller and HaplotypeCaller (GATK 3.3). Candidate genes queried included the known thyroid cancer drivers and the core promoter of TERT including the canonically altered sites in cancer: -124 bp (chr5: l,295.228C>T coordinate), - 146 bp (chr5: 1,295, 250C>T) and -57 bp (chr5: 1,295,161) upstream from the ATG translation start site (coordinates are for hgl9).

[0163] Statistical analyses

[0164] Statistical analyses were performed retrospectively and P-values (all two-sided) were calculated in GraphPad Prism. Example 7 - A subset of individuals with PTC carry rare, deleterious germline variants in telomere maintenance genes

[0165] The sequence of five candidate telomere genes linked to familial cancer [POTI, TINF2, ACD, TERF2IPF and the TERT promoter (TERTp)] in 200 consecutively selected individuals were examined. They represented 189 families and were selected for features that enrich for germline susceptibility including familial, multi-focal and young-onset PTC (Table 7). One-quarter had familial PTC, and a small subset had a history of melanoma (13%) or leukemia / lymphoma (7%) (Table 7). 9 individuals (4.5%) from 7 families were identified with rare, heterozygous pathogenic or likely pathogenic variants (FIGs. 14A-14C and Tables 11-12). They fell in 3 genes: POTI (n=3). TINF2 (n=5) and ACD (n=l) (Tables 11-12). Two unrelated probands shared the POTI c. 1071dup p.Glu358Serfs* 13 variant and its flanking single nucleotide polymorphisms (SNPs), suggesting a potential founder effect (FIG. 14D). The other POTI variant encoded c.121 A>G p.Thr41 Ala, which disrupts a highly conserved residue and is defective for telomeric DNA binding in vitro. The three TINF2 variants were all truncating. TINF2 c.248_249del p.Leu83Glnfs*53 was not reported in the databases (gnomAD v.2.1.1), while TINF2 c.793C>T p.Arg265* was recently reported as a founder variant in Danish individuals with multiple primary melanoma and ultra-long telomere length, and TINF2 c.591del p.Trpl98Glyfs*12 was identified in 3 individuals from the same family who all also fulfilled selection criteria. ACD c.488A>G p.Asnl63Ser was also a familial melanoma founder variant described in Australian and Danish families, and the proband with PTC shared the identical haplotype block (FIG. 14E).

[0166] [Table 11] Pathogenic and likely pathogenic variants identified in candidate telomere genes within Cohort 1 and their prevalence in germline control databases (gnomAD) as well as somatic tumor databases (COSMIC) (identified in 7 probands)

[0167] [Table 12] Pathogenic and likely pathogenic variants in POTI, ACD and TINF2 in PTC

[0168] Cohort 2 Example 8 - Persons with deleterious variants have long telomere length

[0169] 8 probands were recruited and lymphocyte telomere length was measured by flowFISH and it was found that in 5 persons, it was longer than the 90th age-adjusted percentile, including in 3 individuals where it was greater than the 99th percentile (FIG. 14B). For another two individuals, telomere length fell right below the 90th percentile: one carried TINF2 C.793OT p.Arg265*, the recently described founder variant in familial melanoma that is documented in association with ultra-long telomere length. This individual also had a history of multiple myeloma and had undergone autologous bone marrow transplantation for history of a hematologic malignancy, conditions known to secondarily shorten telomere length. The second individual carried the founder ACD variant and had a first degree relative with telomere length longer than the 99th percentile (FIG. 14B). The last individual had telomere length below the 90th percentile with no other evidence of pathogenicity, and this variant (TERF2IP C.650OA p.Pro217Gln), and others in individuals that could not be assessed for telomere length, were classified as variants of uncertain significance (Table 9).

[0170] To test if long telomere length co-segregated with the prioritized variants, 4 additional individuals were recruited from 3 families and found that all 4 who shared variants identified in the probands also had long telomere length, greater than 99th percentile (probability by chance alone less than 1x1 O’8, FIG. 14B). Overall, the pedigrees were consistent with an autosomal dominant, cancer-prone syndrome which showed propensity for multiple cancers that included but w ere not limited to PTC (FIG. 14C and FIG. 15A).

[0171] Example 9 - Prevalence of telomere lengthening variants in unselected PTC

[0172] To test if the findings are generalizable to other individuals with PTC, the prevalence of variants in POTI, TINF2 and ACD was examined in a group of 270 individuals who were unselected for family history or co-occurring cancers. In this cohort, 2.5% fulfilled familial PTC criteria (clinical characteristics in Table 8). 4 individuals (1.5%) w ere identified who carried rare variants that fulfilled the pre-specified criteria of pathogenicity: POTI (n=2), TINF2 (n=l) and ACD (n=l) (FIGs. 14A. 14E). None of these variants had been reported in the gnomAD v.2. 1. 1 database, and all of them had high pathogenicity scores (CADD 23-34, i.e., top 0. l-l%ile of predicted pathogenic variants, Tables 11-12). One POTI missense variant altered p.Lys85, a conserved residue that is somatically mutant in many cancers including angiosarcoma and T cell lymphoma-leukemia (Table 12). TINF2 c. lA>G p.? variant altered the start site, and the ACD variant disrupted a canonical +1 splice site (Table 12). All four individuals had additional non- PTC malignancies including melanoma, renal cell carcinoma, glioblastoma and multiple myeloma (FIGs. 14E and 15A). Their family history also supported a familial cancer-prone syndrome with first degree relatives affected by melanoma and glioblastoma (FIG. 14E). Other individuals with PTC in this cohort had suspicious variants, but they fell outside strict filtering thresholds. For example, two individuals (ages 47 and 62 at PTC diagnosis), who also had second malignancies, carried POTI c. 1127A>G p.Gln376Arg, a variant which had been previously reported in familial chronic lymphocytic leukemia [CLL [MIM: 151400]). Tables 9-10 list these and other variants which were designated as having uncertain significance.

[0173] Example 10 - Individuals with PTC and telomere lengthening variants are prone to non- PTC malignancies including melanoma and lymphoid cancers

[0174] To test if the 22 individuals identified with pathogenic variants have distinguishing clinical features, the cancer history' was examined; the mean age was 64 and this group represented an aggregate of 1,396 person-years (FIG. 15A and Table 13). Mean age at thyroid cancer diagnosis was 48 (range 28-86 years), and remarkably, all the PTCs with data available were multifocal (FIG. 15A and Table 13) Moreover, 78% were male, a preponderance distinct from the known female dominance seen in sporadic PTC, although this enrichment was not statistically significant relative to the populations studied [Relative Risk 1.39 (95% CI, 0.96-1.76), P=0.08, Fisher’s exact test, 15 of 22 males with pathogenic / likely pathogenic variants vs. 230 of 470 males in cohorts 1 and 2 with PTC], Nearly all the individuals identified to have had PTC also had second malignancies (15 of 18, 83%). Cancer was the uniform cause of death (8 of 8 cases), and non-PTC cancers ere the culprit in most cases (6 of 8, Table 13). Melanoma was the most common second cancer (9 of 22, 41%), and in 67% (6 of 9), multiple primary melanoma developed — both cutaneous and ocular (FIGs. 15A-15B). The combined history of PTC-melanoma enriched for the likelihood of identifying a germline pathogenic long telomere-associated variant with a relative risk of 4.54 [(95% confidence interval 1.70-11.23), P=0.009, Fisher’s exact test, 5 of 22 vs. 10 of 200 in cohort 1]. The onset of melanoma was examined relative to PTC, and it was found that they were often separated by years and cases were equally divided between an initial diagnosis of PTC or melanoma, w hile being rarely coincident (FIG. 15C). First degree relatives of PTC probands also had melanoma, further supporting a recognizable syndromic co-occurrence (FIG. 14E and 15A).

[0175] The most common non-melanoma tumors were hematologic malignancies (23%. 5 of 22 individuals). They were predominantly of B cell origin: multiple myeloma (n=2), chronic lymphocytic leukemia (n=l), follicular lymphoma (n=l) (Table 13). Mesenchymal tumors included osteosarcoma and abdominal desmoid (FIG. 15D), epithelial tumors included rectal cancer and multifocal renal clear cell carcinoma (FIG. 15E), and there were cases of PTC cooccurring with glioblastoma and neuroendocrine carcinoma. There was also a range of benign neoplasms spanning tissues in addition to multinodular goiter including hemangioma, hamartoma, lipoma, schwannoma, leiomyoma, pituitary adenoma and multiple polyps in the colon and gallbladder (FIG. 15B), consistent with the pan-neoplasia-prone state associated with long telomere length.

[0176] Example 11 - Genetic anticipation of cancer diagnosis is associated with longer telomere length in offspring

[0177] Telomere length is highly heritable and influenced by parental telomere length. In the autosomal dominant short telomere syndromes, telomere shortening is acquired in successive generations because telomere attrition occurs in the germline. This pattern has been suggested to be more prominent when the parent-of-origin is male because of the highly replicative context of the male germline in mammals. Genetic anticipation of cancer w as recently reported in individuals with pathogenic POTI variants, but did not have access to samples from parent-child pairs where the father was the carrier. Here, the telomere length and cancer phenotypes were examined across 3 parent-child pairs where telomere length was also available and an earlier onset and. at times, an increased number of cancers was found in two families with POTI and ACD variants (FIGs. 16A-16F). In the family with POTI variant c.121 A>G p.Thr41Ala, the proband developed PTC, melanoma, and renal cell cancer by age 46, while his 71 -year-old father subsequently developed melanoma and a pituitary adenoma after age 60 (FIGs. 16A-16B). In the family with the ACD c.488A>G p.Asn!63Ser variant, a 74-y ear-old father developed PTC and an oral cavity neuroendocrine carcinoma after the age of 67, while his son developed osteosarcoma at age 14 (FIGs. 16C-16D). In the third family, the carrier offspring remained unaffected at age 23 (FIG. 16E). Thus, the genetic anticipation is independent of the altered gene and can manifest in discordant cancer diagnoses.

[0178] [Table 13] Clinical features of PTC and cause of death among individuals with telomere- related mutations To test if anticipation was mediated by telomere lengthening, the average telomere length was compared across generations and it was found that it was longer in all three parent-child pairs that were studied (+1.4 kb, +1.8 kb absolute gain in offspring of male parent and +0.9 kb in offspring of a female parent, FIGs. 16A-16F). The lengthening was not explained by the younger age at telomere length measurement of the offspring, as they also had a larger deviation from the age-adjusted median than their parents (i.e.. larger delta telomere length in FIGs. 16B, 16D, 16F). These observations need to be examined in larger groups and are likely influenced also by the nature of the variant (i.e. hypomorphic vs. null). Nonetheless, they support that the sex of the parent of origin and the inherited telomere length may be a determinant of the cancer phenoty pe in these long telomere associated syndromes.

[0179] Example 12 - Telomerase elongation of the shortest telomeres is preserved in the setting of telomere lengthening variants

[0180] The studies so far showed an elongation of the average telomere length, but the shortest telomeres drive the senescence phenotype in vitro and in vivo, because they signal the DNA damage response. It was thus tested if telomere lengthening germline variants affected the preference of telomerase to elongate the shortest telomeres and whether the elongation is ordered for all telomeres or random. Q-FISH was performed on metaphases derived from in vitro stimulated lymphocytes from two parent-child parents. As with the flow cytometry-basis analysis, a longer mean telomere length was found in successive generations (FIGs. 16G-16H). The distributions showed a shift towards higher intensity signals for telomeres of all lengths (FIGs. 16G-16H). Importantly, telomere length followed a homogeneous distribution (FIGs. 16G-16H), as has been seen with telomerase expression in other settings, and the distribution was not bimodal or random consistent with preservation of fundamental homeostatic mechanisms. These data support a model where heterozy gous variants in POTI (and likely the other disease-associated shelterin-encoding genes) remove a negative regulation on telomerase elongation, but telomerase retains its preferential elongation mechanisms and its repeat addition pattern is not random.

[0181] Example 13 - Germline telomere maintenance obviates somatic telomere maintenance mechanisms

[0182] Cancers maintain their immortality by acquiring somatic telomere maintenance mechanisms and in thyroid cancers, TERTp gain-of-function variants are the only known mechanism, seen in up to 15-40% of tumors with the higher prevalence noted in poorly differentiated thyroid cancers. The prevalence of these TERT promoter variants is associated with increasing age and may have a higher incidence in males (TCGA data in FIG. 161). To assess the somatic landscape of the long telomere-associated subset of PTC, a clinical pipeline was used to examine the sequence of 439 genes including the known somatic drivers of PTC in 10 tumors (9 males and 1 female) from 10 unrelated families (FIG. 161). All 10 tumors (100%) had only a single driver variant at BRAF c. 1799T>A p.Val600Glu (FIG. 161), a higher proportion than the 55-70% prevalence seen in unselected PTC cases. Specifically, there were no other driver variants detected in TP53, ATM, RET, mismatch repair or other RAS pathway genes (FIG. 17). Moreover, none of the tumors had a TERTp variant, and there were no variants detected in other cancer-associated telomere maintenance genes including POTI or the alternative lengthening of telomeres (ALT) genes, ATRX and DAXX. These observations raise the compelling possibility that the germline telomere maintenance mutation lowers the threshold to malignancy by obviating a need for a somatic mechanism, in addition to supporting the longevity of somatic oncogenic mutations.

Claims

WHAT IS CLAIMED IS:

1. A method of identifying a subject as having an increased risk of a cancer, the method comprising:(a) determining that the subject exhibits a longer telomere length than a reference subject;(b) detecting the presence of a gene variant in the subject;(c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and(d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as having an increased risk of the cancer.

2. A method of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer, the method comprising:(a) determining that the subject exhibits a longer telomere length than a reference subject;(b) detecting the presence of a gene variant in the subject;(c) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and(d) stratifying the subject based on the subject having long telomere length, the gene variant, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as being in need of cancer screening for early detection or prevention of the cancer.

3. A method of identifying a subject to be in need of cancer screening for early detection or prevention of a cancer, the method comprising:(a) determining that the subject exhibits a longer telomere length than a reference subject;(b) detecting the presence of a gene variant in the subject;(c) determining that the subject exhibits an increased T-cell receptor (TCR) or a B- cell receptor (BCR) clonality than a reference subject;(d) detecting a disease-indicative phylogenetic pattern of a single-colony hematopoietic lineage in the subject; and(e) stratifying the subject based on the subject having long telomere length, the gene variant, increased TCR or BCR clonality, and the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject to be in need of cancer screening for early detection or prevention of the cancer.

4. The method of any one of claims 1-3, wherein the gene variant comprises a POTI mutation, a TERT promoter mutation, an ACD / TPP1 mutation, a TINF2 mutation, a TERFI mutation, or a RAPl mutation.

5. The method of any one of claims 1-4, wherein the determining step (a) comprises sequencing.

6. The method of claim 5, wherein the sequencing comprises whole genome sequencing.

7. The method of claim 6, wherein the determining step (a) further comprises flow cytometry and fluorescence in-situ hybridization (flow-FISH).

8. The method of any one of claims 1-7, wherein the long telomere length comprises a telomere length longer than an age-adjusted reference subject.

9. The method of any one of claims 1-8, wherein detecting the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage comprises genome sequencing.

10. The method of any one of claims 1 -9, wherein detecting the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage comprises one or more of assessing the number of independent clonal mutations, estimated onset, mutation burden, mutational signature, and / or overall clonality in the subject.

11. The method of any one of claims 1-10, wherein the cancer is a benign solid or hematologic neoplasm.

12. The method of claim 11, wherein the cancer comprises melanoma, thyroid cancer, lung cancer, neuronal cancer, glioma, parotid and parathyroid adenoma, angiosarcoma, osteosarcoma, soft tissue sarcoma, bladder cancer, kidney cancer, colorectal cancer, lymphoma, or leukemia.

13. The method of any one of claims 1-12, wherein the cancer is a malignant solid neoplasm.

14. The method of claim 13, wherein the cancer comprises gliomas and other neuronal cancers, thyroid cancers, sarcoma, epithelial malignancies, colorectal cancer, renal cell cancer, or lung cancer.

15. A method of identifying a subject as a T-cell or bone marrow donor, the method comprising:(a) determining that the subject exhibits a medium telomere length;(b) detecting the absence of a gene variant in the subject;(c) determining that the subject has a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject;(d) detecting the absence of a disease-indicative phylogenetic pattern of a singlecolony hematopoietic lineage in the subject; and(e) stratifying the subject based on the subject having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of the disease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as a T-cell or bone marrow donor.

16. A method of stratifying a subject as a transplant recipient, the method comprising:(a) determining that the subject exhibits a medium telomere length;(b) detecting the absence of a gene variant in the subject ;(c) determining that the subject has a normal T-cell receptor (TCR) or a B-cell receptor (BCR) clonality than a reference subject;(d) detecting the absence of a disease-indicative phylogenetic pattern of a singlecolony hematopoietic lineage in the subject; and(e) stratifying the subject based on the subject having medium telomere length, absence of the gene variant, normal TCR or BCR clonality, and absence of thedisease-indicative phylogenetic pattern of the single-colony hematopoietic lineage, thereby identifying the subject as a transplant recipient.

17. The method of claim 16, wherein the transplant comprises hematopoietic stem cell transplant.

18. The method of any one of claims 15-17. wherein the gene variant comprises a POTI mutation, a TERT promoter mutation, an ACD / TPP1 mutation, a TINF2 mutation, a TERFI mutation, or a RAPl mutation.

19. The method of any one of claims 15-18, wherein the determining step (a) comprises sequencing.

20. The method of claim 19, wherein the sequencing comprises whole genome sequencing.

21. The method of claim 19, wherein the determining step (a) further comprises flow cytometry and fluorescence in-situ hybridization (flow-FISH).

22. The method of any one of claims 15-21. wherein detecting the absence of diseaseindicative phylogenetic pattern of the single-colony hematopoietic lineage comprises genome sequencing.

23. The method of any one of claims 15-22, wherein detecting the absence of diseaseindicative phylogenetic pattern of the single-colony hematopoietic lineage comprises one or more of assessing the number of independent clonal mutations, estimated onset, mutation burden, mutational signature, and / or overall clonality in the subject.

Images