Methods and systems for measuring multiple cell states - Patents.com

Abstract

Methods and systems for detecting cellular conditions in biological samples are disclosed. Methods and systems for predicting therapeutic response, severe immune-related adverse events (irAEs), symptomatic irAEs, and irAE grades of a subject to be administered immunotherapeutic treatment based on cellular conditions detected from a single biological sample of the subject are also disclosed. The present disclosure generally relates to methods for detecting multiple cellular conditions in a body fluid or a nucleic acid mixture.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 320,927, filed March 17, 2022, which is incorporated by reference in its entirety.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with Government support under CA238711 and CA142710 awarded by the Institutes of Health. The Government has certain rights in this invention.

[0003] Reference materials N / A

[0004] FIELD OF THEINVENTION The present disclosure relates generally to methods for detecting multiple cellular states in bodily fluids or nucleic acid mixtures. Summary of the Invention [Means for solving the problem]

[0005] Summary of the Invention Among various aspects of the present disclosure, methods and systems for detecting a cellular state are provided.

[0006] In one aspect, a method for determining cell state composition from a biological sample is disclosed, the method comprising the steps of: providing the biological sample containing cell-free DNA, the cell-free DNA comprising a plurality of cell-free DNA fragments; providing a ground truth reference table comprising a plurality of reference cell and tissue states and associated reference methylation levels; identifying CpGs in each DNA fragment of the cell-free DNA to determine the methylation level associated with each DNA fragment; comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; assigning each DNA fragment to the cell or tissue state from the ground truth reference table that has the most similar associated reference methylation level to the methylation level of the DNA fragment; counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and determining the cell state composition based on the read-count table. In some aspects, the biological sample is a blood sample. In some aspects, the reference methylation value comprises differentially methylated CpGs from DNA of bacterial, viral, fungal, or eukaryotic parasite origin, which may occur from known cell types and known cell states. In some aspects, the cell-free DNA is derived from plasma. In some aspects, the cell state composition comprises at least two cell types, and each cell type comprises at least two cell states. In some aspects, the method further comprises inferring melanoma tumor fraction, cell-infiltrating leukocyte fraction, CD4 TEM level, and any combination thereof based on the cell state composition.

[0007] In another aspect, a method is disclosed of predicting a therapeutic response of a subject to be administered an immunotherapeutic treatment, the method comprising: obtaining a biological sample from the subject comprising cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; determining the cell state composition of the subject using a method as disclosed herein; inferring a melanoma tumor fraction based on the cell state composition; and predicting a response to the immunotherapeutic treatment based on the melanoma tumor fraction.

[0008] In another aspect, a method of predicting a therapeutic response of a subject to be administered an immunotherapeutic treatment is disclosed, the method comprising the steps of obtaining a biological sample from the subject comprising cell-free DNA, the cell-free DNA comprising a plurality of cell-free DNA fragments; determining the cell state composition of the subject using a method as disclosed herein; inferring a tumor cell infiltrating leukocyte fraction based on the cell state composition; and predicting a response to the immunotherapeutic treatment based on the tumor cell infiltrating leukocyte fraction.

[0009] In another aspect, a method is disclosed for predicting the severity of an immune-related adverse event in a subject to be administered an immunotherapeutic treatment, the method comprising: obtaining a biological sample from the subject comprising cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; determining the cellular state composition of the subject using a method as disclosed herein; inferring a CD4 TEM fraction based on the cellular state composition; and predicting the severity of the immune-related adverse event based on the CD4 TEM fraction.

[0010] In another aspect, a method is disclosed for predicting symptomatic immune-related adverse events in a subject to be administered an immunotherapeutic treatment, the method comprising: obtaining a biological sample from the subject comprising cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; determining the cell state composition of the subject using a method as disclosed herein; inferring a CD4 TEM fraction based on the cell state composition; and predicting the symptomatic irAE based on the CD4 TEM fraction.

[0011] In another aspect, a method is disclosed of predicting the grade of an immune-related adverse event in a subject to be administered an immunotherapeutic treatment, the method comprising: obtaining a single biological sample from the subject comprising cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; determining the cellular state composition of the subject using a method as disclosed herein; inferring a CD4 TEM fraction based on the cellular state composition; and predicting the grade of the immune-related adverse event based on the CD4 TEM fraction.

[0012] In another aspect, a method is disclosed for predicting the therapeutic response grade, severe immune-related adverse events (irAEs), symptomatic irAEs, irAE grades, and any combination thereof of a subject to be administered immunotherapy treatment, comprising the steps of: obtaining a sample from the subject that contains cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; determining the cell state composition of the subject using the method as disclosed herein; and predicting melanoma tumor fraction, cell-infiltrating leukocyte fraction, and CD4 TEM fraction based on the cell state composition. The method further comprises predicting at least one of the following: response to the immunotherapy treatment based on at least one of the melanoma tumor fraction and the cell-infiltrating leukocyte fraction; and severe immune-related adverse events (irAEs), symptomatic irAEs, irAE grades, and any combination thereof based on the CD4 TEM fraction.

[0013] Other objects and features will be in part apparent and in part pointed out hereinafter. [Brief description of the drawings]

[0014] Those skilled in the art will understand that the drawings, described below, are for illustration purposes only and are not intended to limit the scope of the present teachings in any way.

[0015] [Figure 1] FIG. 1 is a block diagram that diagrammatically illustrates a system according to one aspect of the present disclosure.

[0016] [Diagram 2] FIG. 2 is a block diagram that diagrammatically illustrates a computing device according to one aspect of the present disclosure.

[0017] [Diagram 3] FIG. 3 is a block diagram that diagrammatically illustrates a remote or user computing device according to one aspect of the present disclosure.

[0018] [Figure 4] FIG. 4 is a block diagram that diagrammatically illustrates a server system according to one aspect of the present disclosure.

[0019] [Diagram 5] Figure 5 shows the number of differentially methylated CpGs per purified cell state. Cell states were purified from peripheral blood or tumor tissues and sequenced by genome-wide next-generation methylation sequencing, and then differentially methylated region (DMR) analysis was performed by bioinformatics. The number of differentially methylated CpGs per cell state at different delta thresholds is shown here. Delta indicates DMR calling stringency, with 0.05 being the least stringent and 0.9 being the most stringent.

[0020] [Figure 6A] Figure 6A is a graph quantifying melanoma tumor fraction in cell-free DNA from plasma samples derived from melanoma patients with sustained clinical benefit (DCB, response) or without sustained benefit (NDB, non-response) from immune checkpoint inhibitor (ICI) treatment. Plasma was extracted from pre-treatment blood samples derived from melanoma patients treated with immune checkpoint blockade. After plasma extraction, cell-free DNA was analyzed for the presence of melanoma tumor signal in plasma cell-free DNA using next-generation methylation sequencing followed by read-counting.

[0021] [Figure 6B] FIG. 6B is a graph of the sensitivity and specificity of the ability to predict response from the data shown in FIG. 6A.

[0022] [Figure 7A]Figure 7A is a graph quantifying the tumor infiltrating leukocyte (TIL) fraction (ctilDNA fraction) in cell-free DNA from plasma samples derived from melanoma patients with sustained clinical benefit (DCB, response) or without sustained benefit (NDB, non-response) from immune checkpoint inhibitor (ICI) treatment. Plasma was extracted from pre-treatment blood samples derived from melanoma patients treated with immune checkpoint blockade. After plasma extraction, cell-free DNA was analyzed for the presence of TIL signal in plasma cell-free DNA using next-generation methylation sequencing followed by read-counting.

[0023] [Figure 7B] FIG. 7B is a graph of the sensitivity and specificity of the ability to predict response from the data shown in FIG. 7A.

[0024] [Figure 8A] Figure 8A is a graph quantifying the CD4 T effector memory (TEM) fraction of cell-free DNA from plasma samples derived from melanoma patients without or with severe immune-related adverse effects (irAEs) from immune checkpoint inhibitor (ICI) immunotherapy. Plasma was extracted from pre-treatment blood samples derived from melanoma patients treated with immune checkpoint blockade. After plasma extraction, cell-free DNA was analyzed for the presence of CD4 TEM signal in plasma cell-free DNA using next-generation methylation sequencing followed by read-counting.

[0025] [Figure 8B] FIG. 8B is a graph of the sensitivity and specificity of the ability to predict serious immune-related adverse events from the data shown in FIG. 8A.

[0026] [Figure 9A]FIG. 9A is a graph quantifying CD4 T effector memory (TEM) cell levels of cell-free DNA from plasma samples derived from melanoma patients with or without symptomatic immune-related adverse effects (irAEs) from immune checkpoint inhibitor (ICI) immunotherapy.

[0027] [Figure 9B] FIG. 9B is a graph of the sensitivity and specificity of the ability to predict symptomatic immune-related adverse events from the data shown in FIG. 9A.

[0028] [Figure 10] FIG. 10 is a graph quantifying the CD4 T effector memory (TEM) cell fraction of cell-free DNA plasma samples from melanoma patients on a graded basis (0-4) of immune-related adverse effects (irAEs). This shows that the described method can predict irAEs on a graded basis. Plasma was extracted from pre-treatment blood samples from melanoma patients treated with immune checkpoint blockade. After plasma extraction, cell-free DNA was analyzed for the presence of CD4 TEM signal in plasma cell-free DNA using next-generation methylation sequencing followed by read-counting, which clinically correlated with immune-related adverse event (irAE) severity (irAE grade as measured by CTCAE v5).

[0029] [Figure 11] FIG. 11 is a plot showing the expression of various cell states and types in relation to the severity of irAEs present in patients, showing that CD4 TEM is most significantly associated with severe irAEs compared to other cell states and types. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Detailed Description of the Invention The present disclosure is based, at least in part, on the discovery that cell state can be measured in tissue or body fluid.It is noted that the scope of the above method is not limited to DNA methylation or cell-free DNA from plasma.It can be applied to any sequenced nucleic acid mixture (i.e., DNA or RNA) from any cell or cell-free DNA source (i.e., any body fluid or tissue source). The examples disclosed herein use bisulfite / methylation sequencing, however, the methods may be used with any type of next-generation sequencing or microarray technology known in the art (see, e.g., Rajesh et al. 2017 - Next-Generation Sequencing Methods; Current Developments in Biotechnology and Bioengineering: Functional Genomics and Metabolic Engineering 2017, Pages 143-158; Moss et al. 2018 Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun 9, 5068; Bumgarner, 2013, Overview of DNA Microarrays: Types, Applications, and Their Future, Volume101, Issue 1 Pages 22.1.1-22.1.11).

[0031] As shown herein, the method of the present disclosure allows for the detection and profiling of the tumor microenvironment (including tumor-infiltrating leukocytes and tumor cell status) using a blood-based liquid biopsy approach. This is done via methylation sequencing of cell-free DNA derived from plasma. Individual single cell status is profiled from the bulk using either genome-wide or targeted bi-cytoplasmic sequencing (e.g., leukocyte and tumor cell status by counting or optionally deconvoluting plasma methylation sequencing data).

[0032] This method is based on single molecule counting, which allows counting and sorting molecules (DNA or RNA reads) into reference bins at a molecule-by-molecule level. Thus, the method requires a counting step. It starts with individual molecules, and by counting and sorting them one by one, the method can learn how the whole system is organized molecule by molecule. This can make this method high-resolution compared to alternative methods.

[0033] In some embodiments, machine learning models can be used to count and classify DNA or RNA molecules into reference bins.In these embodiments, the machine learning models can be trained using DNA or RNA molecules obtained from cell types or cell states isolated as described herein.Any machine learning configuration can be used to implement the methods disclosed herein, including but not limited to random forest, support vector machine, logistic regression, KNN, and K-means.In some aspects, gradient boosting and AdaBoosted algorithms can also be applied to further optimize read-counting algorithms when implemented using machine learning systems and methods.

[0034] Deconvolution, on the other hand, starts by looking at the complete bulk sequenced mixture as a whole, and then attempts to weight and add together the cell type specific signatures to achieve a matrix that optimally represents the mixture. Thus, deconvolution methods inherently have a much lower resolution and are fundamentally different from the methods of this disclosure.

[0035] In various aspects, a read-counting method for deconvoluting cell-free DNA methylation is disclosed, which provides the determination of multiple cell states (≧2). The read-counting method provides exquisite granularity with the ability to distinguish and quantify several cell states (even related ones) from each other. The read-counting method of the present disclosure can be used to non-invasively profile tumors and tumor microenvironments from body fluid samples, and the cell states identified using the read-counting method can be used to non-invasively predict treatment response via "liquid biopsy".

[0036] In some aspects, the reading-counting method comprises identifying CpG at a per-fragment level in cell-free DNA.The term "CpG" as used herein refers to the nucleotide sequence cytosine-guanine (CG) where methylation commonly occurs.In some aspects, methylation level (defined herein as the portion of CpG sites that are actively methylated) is measured.In various aspects, methylation level can be measured using any suitable method, including but not limited to bisulfite / methylation sequencing.

[0037] In various aspects, the methylation levels of CpGs can be compared to a ground truth reference table of known cell / tissue states, and the CpG sites per fragment can be matched to assign cell-free DNA fragments to cell states in the reference table. In some aspects, each ground truth reference table is obtained by analyzing cell-free DNA samples obtained from a source with a known single cell type and / or single cell state, including but not limited to the various cells and cell states described herein. The cell-free DNA fragments are counted until all fragments have been evaluated and assigned to the ground truth reference table. In some embodiments, the results can be further optimized using machine learning. The pattern of assignment of cell-free DNA fragments from the cell-free DNA mixture can be used to determine the cell state composition of the cell-free DNA mixture. In some embodiments, the methylation level can be represented by the number of CpG sites. In some embodiments, the methylation level can be represented as the fraction of CpG sites that are actively methylated as measured by bisulfite / methylation sequencing.

[0038] It is specifically shown that immunotherapy toxicity (and more generally treatment toxicity) can be predicted from cell-free DNA methylation cell state analysis. Peripheral blood cell state can be granularly profiled using the above method, and activated CD4 T effector memory cells can be quantified from cell-free DNA, which allows early prediction of immunotherapy toxicity (i.e., immune-related adverse events) pre- and during treatment. Furthermore, the above method can simultaneously predict both treatment response and toxicity using the same assay. Furthermore, the above assay can be applied to simultaneously quantify a wide range of cell states, essentially an atlas, comprehensively representative of human health and disease (i.e., by measuring a large number of cell, tissue, and microbial types / states that are either sequenced by us or present in public / published methylation datasets), and predict and monitor risk for a wide range of physiological conditions, disorders, infections, and diseases.

[0039] This method can enumerate and distinguish cell types and / or cell states without the need for solid tissue biopsies. A "cellular state" can be defined as a context-dependent version of a given cell type (e.g., normal vs. tumor-associated CD8 T cells). This unique capability allows the disclosed non-invasive approach to measure non-malignant cells within tumors and distinguish tumors from their normal tissue counterparts. It is currently believed to be the first time this has been accomplished. Previous work has focused exclusively on distinguishing cell types, tissue types, and cancer vs. normal cells -- all of these classifications are less granular than cellular state.

[0040] The disclosed method relies on prior knowledge (e.g., from known cells) of cell state-specific signatures. These signatures allow the approach to enumerate specific cell types and cell states directly from methylation signals in cell-free DNA. Such signatures can be derived by physically isolating the cell states of interest by FACS, or by inferring them via single-cell bisulfite sequencing. However, these methods have significant drawbacks, including variability in the loss of specific cell types due to tissue dissociation, the sensitivity and specificity of antibody panels (required for FACS), and the small amount of tissue typically obtained from tumor biopsies. Thus, novel alternative methods have been developed to complement these techniques. The approach is based on directly inferring cell state signatures from bulk tumor methylation profiles. This can be done via statistical deconvolution in a process that is essentially the reverse of measuring cellular composition from bulk methylation profiles (e.g., CIBERSORTx; Newman et al. (2019) Nature Biotechnology (37) 773-782). This novel approach can be used to flexibly generate signatures for nearly any cellular state of interest without antibodies, living cells, or physical cell isolation.

[0041] The above reading-counting method allows high-resolution methylation cell state analysis of plasma cell-free DNA, which is used in the present disclosure to identify 24 distinct cell states in plasma.As described herein, the above method can simultaneously predict immunotherapy response and toxicity from the same plasma sample and sequencing results.In some embodiments, this immunotherapy response and toxicity prediction can be performed before treatment.These methods also allow the use of these methods in clinical settings.

[0042] It is noted that the scope of the above method is not limited to DNA methylation or cell-free DNA derived from plasma. It can be adapted to any sequenced nucleic acid mixture from any cell or cell-free DNA or RNA source (i.e., any body fluid or tissue source).

[0043] Method and system for non-invasively measuring cellular status in body fluids - Patents.com The present disclosure provides a non-invasive method for measuring cell states in bodily or biological fluids, more specifically, enumerating specific cell types and cell states directly from methylation signals present in cell-free DNA.

[0044] As described herein, this technology can identify cell type and cell state in single cells or bulk mixtures of cells.Cell state can be defined as the phenotype of cell.Cell phenotype can be "homeostatic phenotype", which suggests plasticity resulting from dynamically changing yet characteristic patterns of gene / protein expression.

[0045] The methods described herein may be applied to many commercial / biomedical problems including immunotherapy response assessment, immunotherapy toxicity assessment, response of any tumor to any drug, non-invasively tracking the tumor microenvironment in research, clinical or commercial applications, and enabling true liquid biopsy of tumors including both cancer and tumor microenvironment profiling.

[0046] This technology can be used in a wide variety of applications, using any type of epigenetic data (i.e., whole-genome bisulfite sequencing, reduced-representation bisulfite sequencing, methylation microarrays, etc.) on any bodily fluid (e.g., urine, saliva, plasma, feces, etc.).

[0047] This method allows for the detection and profiling of the tumor microenvironment (including tumor-infiltrating leukocytes and tumor cell states) using a liquid biopsy approach. We do this through methylation sequencing of cell-free DNA derived from plasma, followed by digital cytometry (deconvolution). We profiled individual single cell states from the bulk (e.g., leukocyte and tumor cell states by deconvoluting plasma methylation sequencing data) using either genome-wide or targeted bisulfite sequencing.

[0048] Although this method is shown here to detect cell state and cell type in cell-free DNA, it can also be a useful method for use with any length of nucleic acid sequencing. The nucleic acid can be full-length DNA, DNA fragments, cell-free DNA, RNA, or cell-free nucleic acid fragments that are assigned to cell types originating from tumor cells, infected cells, damaged cells, normal cells, bacterial cells, organ or tissue cells, tissue cells secreting cfDNA, microorganisms (e.g. bacteria, viruses (DNA or RNA), fungi, or eukaryotic parasites). In some embodiments, the DNA fragments can be about 300 base pairs or less. It is also noted that the scope of the method is not limited to DNA methylation or cell-free DNA derived from plasma. It can be applied to any sequenced or microarray profiled nucleic acid mixture derived from any cell or cell-free DNA source (i.e. any body fluid or tissue source).

[0049] As described herein, one or more CpG methylation sites are detected. The CpG methylation sites can be associated together between any number of base pairs (e.g., close to or near each other) along the length of the DNA molecule. In some embodiments, the number of base pairs between CpGs that can be associated together can be between about 1 base pair (bp) and about 1000 bp (close to or near each other), between 1 bp and about 500 bp, or between about 1 bp and about 300 bp. For example, nearby or proximal CpGs may be about 1 bp; about 2 bp; about 3 bp; about 4 bp; about 5 bp; about 6 bp; about 7 bp; about 8 bp; about 9 bp; about 10 bp; about 11 bp; about 12 bp; about 13 bp; about 14 bp; about 15 bp; about 16 bp; about 17 bp; about 18 bp; about 19 bp; about 20 bp; about 21 bp; about 22 bp; about 23 bp; about 24 bp; about 25 bp; about 26 bp; about 27 bp; about 28 bp; about 29 bp; about 30 bp; about 31 bp; about 32 bp; about 33bp; about 34bp; about 35bp; about 36bp; about 37bp; about 38bp; about 39bp; about 40bp; about 41bp; about 42bp; about 43bp; about 44bp; about 45bp; about 46bp; about 47bp; about 48bp; About 49bp; About 50bp; About 51bp; About 52bp; About 53bp; About 54bp; About 55bp; About 56bp; About 57bp; About 58bp; About 59bp; About 60bp; About 61bp; About 62bp; About 63bp; About 64bp; bp; about 66bp; about 67bp; about 68bp; about 69bp; about 70bp; about 71bp; about 72bp; about 73bp; about 74bp; about 75bp; about 76bp; about 77bp; about 78bp; about 79bp; about 80bp; about 81bp; About 82bp; About 83bp; About 84bp; About 85bp; About 86bp; About 87bp; About 88bp; About 89bp; About 90bp; About 91bp; About 92bp; About 93bp; About 94bp; About 95bp; About 96bp; About 97bp; About 98 bp; about 99bp; about 100bp; about 101bp; about 102bp; about 103bp; about 104bp; about 105bp; about 106bp; about 107bp; about 108bp; about 109bp; about 110bp; about 111bp; about 112bp approximately 113bp; approximately 114bp; approximately 115bp; approximately 116bp; approximately 117bp; approximately 118bp; approximately 119bp; approximately 120bp; approximately 121bp; approximately 122bp; approximately 123bp; approximately 124bp; approximately 125bp; approximately 126bp;Approximately 127 bp; approximately 128 bp; approximately 129 bp; approximately 130 bp; approximately 131 bp; approximately 132 bp; approximately 133 bp; approximately 134 bp; approximately 135 bp; approximately 136 bp; approximately 137 bp; approximately 138 bp; approximately 139 bp; approximately 140 bp; approximately 141 bp; approximately 142 bp; approximately 143 bp; approximately 144 bp; approximately 145 bp; approximately 146 bp; approximately 147 bp; approximately 148 bp; approximately 149 bp; approximately 150 bp; approximately 151 bp; approximately 152 bp; approximately 153 bp; approximately 154 bp; approximately 155 bp; approximately 156 bp; approximately 157 bp; approximately 158 bp; approximately 159 bp; approximately 160 bp; approximately 161 bp; approximately 162 bp; approximately 163 bp; approximately 164 bp; approximately 165 bp; approximately 166 bp; approximately 167 bp; approximately 168 bp; approximately 169 bp; approximately 170 bp; approximately 171 bp; approximately 172 bp; approximately 173 bp; approximately 174 bp; approximately 175 bp; approximately 176 bp; approximately 177 bp; approximately 178 bp; approximately 179 bp; approximately 180 bp; approximately 181 bp; approximately 182 bp; approximately 183 bp; approximately 184 bp; approximately 185 bp; approximately 186 bp; approximately 187 bp; approximately 188 bp; approximately 189 bp; approximately 190 bp; approximately 191 bp; approximately 192 bp; approximately 193 bp; approximately 194 bp; approximately 195 bp; approximately 196 bp; approximately 197 bp; approximately 198 bp; approximately 199 bp; approximately 200 bp; approximately 201 bp; approximately 102 bp; approximately 203 bp; approximately 204 bp; approximately 205 bp; approximately 206 bp; approximately 207 bp; approximately 208 bp; approximately 209 bp; approximately 210 bp; approximately 211 bp; approximately 212 bp; approximately 213 bp; approximately 214 bp; approximately 215 bp; approximately 216 bp; approximately 217 bp; approximately 218 bp; approximately 219 bp; approximately 220 bp; approximately 221 bp; approximately 222 bp; approximately 223 bp; approximately 224 bp; approximately 225 bp; approximately 226 bp; approximately 227 bp; approximately 228 bp; approximately 229 bp; approximately 230 bp; approximately 231 bp; approximately 232 bp; approximately 233 bp; approximately 234 bp; approximately 235 bp; approximately 236 bp; approximately 237 bp; approximately 238 bp; approximately 239 bp; approximately 240 bp; approximately 241 bp; approximately 242 bp; approximately 243 bp; approximately 244 bp; approximately 245 bp; approximately 246 bp; approximately 247 bp; approximately 248 bp; approximately 249 bp; approximately 250 bp; approximately 251 bp; approximately 252 bp; approximately 253 bp; approximately 254 bp; approximately 255 bp; approximately 256 bp; approximately 257 bp; approximately 258 bp; approximately 259 bp; approximately 260 bp; approximately 261 bp; approximately 262 bp; approximately 263 bp; approximately 264 bp; approximately 265 bp; approximately 266 bp; approximately 267 bp; approximately 268 bp; approximately 269 bp;It may be separated by about 270 bp; about 271 bp; about 272 bp; about 273 bp; about 274 bp; about 275 bp; about 276 bp; about 277 bp; about 278 bp; about 279 bp; about 280 bp; about 281 bp; about 282 bp; about 283 bp; about 284 bp; about 285 bp; about 286 bp; about 287 bp; about 288 bp; about 289 bp; about 290 bp; about 291 bp; about 292 bp; about 293 bp; about 294 bp; about 295 bp; about 296 bp; about 297 bp; about 298 bp; about 299 bp; or about 300 bp.

[0050] The control sample or reference sample as described herein can be a sample from a healthy subject. Reference value can be used instead of the control or reference sample (which is obtained beforehand from a healthy subject or a group of healthy subjects). The control sample or reference sample can also be a sample with known cell or tumor composition.

[0051] Computing Systems and Devices In various aspects, the methods described herein are implemented using computing devices and systems. FIG. 1 shows a simplified block diagram of a system 800 for implementing the methods described herein. As illustrated in FIG. 1, the system 800 may be configured to implement at least a portion of the tasks associated with the methods of the present disclosure. The system 800 may include a computing device 802. In one aspect, the computing device 802 is part of a server system 804, which also includes a database server 806. The computing device 802 is in communication with a database 808 via the database server 806 over a network. The network 850 may be any network that allows local or wide area communication between devices. For example, the network 850 may allow communication coupling to the Internet via at least one of many interfaces, including but not limited to at least one of the following networks: the Internet, a local area network (LAN), a wide area network (WAN), an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular connection, and a cable modem. The user computing device 830 may be any device that can access the Internet, including, but not limited to, a desktop computer, a laptop computer, a personal digital assistant (PDA), a mobile phone, a smartphone, a tablet, a phablet, a wearable electronic device, a smart watch, or other web-based connectable device or mobile device.

[0052] In other aspects, the computing device 802 is configured to perform a number of tasks associated with methods of detecting cell states and / or cell type abundances as described herein. FIG. 2 shows a component configuration 400 of a computing device 402, which includes a database 410 along with other associated computing components. In some aspects, the computing device 402 is similar to the computing device 802 (shown in FIG. 1). A user 404 may access the components of the computing device 402. In some aspects, the database 420 is similar to the database 808 (shown in FIG. 1).

[0053] In one aspect, the database 410 includes library data 418, algorithm data 412, ML model data 416, and sample data 420. In one aspect, the library data 418 includes library entries that define characteristics of different cell types or cell states whose abundances are detected as described herein. Non-limiting examples of library data 418 include CpG library entries, methylation haplotype block (MHB) library entries, and signature matrices. As used herein, a CpG library is defined as a plurality of entries, each of which includes a differentially methylated CpG site that is indicative of one of a cell type or cell state. In some aspects, the differentially methylated CpG sites are further co-associated CpG sites. As used herein, co-associated CpG sites refer to differentially methylated CpG sites that characterize one of a cell type or cell state that are located at a distance of about 200 bp or less from additional differentially methylated CpG sites that characterize the same cell type or cell state. As used herein, MHB library is defined as a plurality of entries, each of which comprises at least two co-associated CpG sites, each of which represents one of cell types or cell states.As used herein, signature matrix comprises a plurality of differentially methylated CpG sites that characterize all or one of cell types or cell states.The signature matrix is ​​used as part of digital deconvolution method as described herein.Non-limiting examples of suitable digital deconvolution methods include CIBERSORTx.

[0054] In various aspects, the algorithm data 412 includes any parameters used to implement the methods as described herein. Non-limiting examples of suitable algorithm data 412 include any values ​​of parameters that define the calculation of abundance counts, relative abundances, absolute abundances, and any other related parameters. Non-limiting examples of ML model data 416 include any values ​​of parameters that define a machine learning model used to perform digital deconvolution, and any other transformation, classification, or other task according to the methods described herein to optimize a CpG library. Non-limiting examples of sample data 420 include any multiple reads (including DNA sequences, RNA sequences, DNA methylation sequences, and any other suitable nucleic acid sequences) associated with biological sample analysis according to the methods described herein.

[0055] The computing device 402 also includes a number of components that perform specific tasks. In an exemplary aspect, the computing device 402 includes a data storage device 430, an abundance component 440, an analysis component 450, an ML component 470, and a communication component 460. The data storage device 430 is configured to store data received or generated by the computing device 402 (e.g., any of the data stored in the database 410 or any output of a process implemented by any component of the computing device 402). The abundance component 450 is configured to convert a plurality of readings associated with a sample into at least one abundance, at least one relative abundance, at least any absolute abundance, or any combination thereof, for each of one of the cell types or cell states to be detected according to the methods described herein. The analysis component 450 is configured to perform any further analysis of any of the abundances generated according to the methods described. Non-limiting examples of further analysis performed using the analysis component 450 include diagnosis of disease or disorder (e.g., cancer or sepsis), classification of patients into categories such as responders or non-responders to treatment, determination of treatment efficacy, and any other suitable analysis. In various aspects, the ML component 470 is configured to implement any of the machine learning model-based transformations and analyses as described herein. Non-limiting examples of transformations or analyses implemented using the ML component 470 include digital deconvolution of cell types or cell states based on multiple reads in a mixed sample. Optimization of CpG or MHB libraries, or any other suitable transformations or analyses follow the methods described herein.

[0056] The communications component 460 is configured to enable communications of the computing device 402 over a network (e.g., network 850 (shown in FIG. 1)) or multiple network connections using a predefined network protocol (e.g., TCP / IP (Transmission Control Protocol / Internet Protocol)).

[0057] FIG. 3 illustrates a configuration of a remote or user computing device 502 (e.g., user computing device 830 (shown in FIG. 1)). The computing device 502 may include a processor 505 for executing instructions. In some aspects, executable instructions may be stored in a memory area 510. The processor 505 may include one or more processing units (e.g., in a multi-core configuration). The memory area 510 may be any device that provides information such as executable instructions and / or other data to be stored and retrieved. The memory area 510 may include one or more computer-readable media.

[0058] The computing device 502 may also include at least one media output component 515 for presenting information to the user 501. The media output component 515 may be any component capable of conveying information to the user 501. In some aspects, the media output component 515 may include an output adapter (e.g., a video adapter and / or an audio adapter). The output adapter may be operably coupled to the processor 505 and may be operably coupled to an output device (e.g., a display device (e.g., a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a cathode ray tube (CRT), or an “electronic ink” display) or an audio output device (e.g., a speaker or headphones). In some aspects, the media output component 515 may be configured to present an interactive user interface (e.g., a web browser or a client application) to the user 501.

[0059] In some aspects, the computing device 502 may include an input device 520 for accepting input from the user 501. The input device 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch panel (e.g., a touchpad or touch screen), a camera, a gyroscope, an accelerometer, a position detector, and / or an audio input device. A single component (e.g., a touch screen) may function as both an output device of the media output component 515 and an input device 520.

[0060] The computing device 502 may also include a communications interface 525, which may be communicatively coupleable to a remote device. The communications interface 525 may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a cellular network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth) or other mobile data network (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).

[0061] Computer readable instructions are stored in memory area 510 for providing a user interface to user 501 via, for example, media output component 515, and, if necessary, accepting and processing input from input device 520. The user interface may include, among other possibilities, a web browser and client applications. The web browser allows user 501 to display and interact with media and other information typically embedded in web pages or websites from a web server. The client applications allow user 501 to interact with server applications, for example, associated with a merchant or business.

[0062] 4 illustrates an example configuration of a server system 602. The server system 602 may include, but is not limited to, a database server 806 and a computing device 802 (both shown in FIG. 1). In some aspects, the server system 602 is similar to the server system 804 (shown in FIG. 1). The server system 602 may include a processor 605 for executing instructions. The instructions may be stored, for example, in a memory area 625. The processor 605 may include one or more processing units (e.g., in a multi-core configuration).

[0063] The processor 605 may be operably coupled to a communication interface 615 such that the server system 602 may communicate with a remote device, such as a user computing device 830 (shown in FIG. 1) or another server system 602. For example, the communication interface 615 may accept requests from a user computing device 830 via a network 850 (shown in FIG. 1).

[0064] The processor 605 may also be operably coupled to a storage device 625. The storage device 625 may be any computer-operated hardware suitable for storing and / or retrieving data. In some aspects, the storage device 625 may be integrated into the server system 602. For example, the server system 602 may include one or more hard disk drives as the storage device 625. In other aspects, the storage device 625 may be external to the server system 602 and may be accessed by multiple server systems 602. For example, the storage device 625 may include multiple storage devices (e.g., hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration). The storage device 625 may include a storage area network (SAN) and / or a network-attached storage (NAS) system.

[0065] In some aspects, the processor 605 may be operably coupled to a storage device 625 via a storage interface 620. The storage interface 620 may be any component that may provide the processor 605 with access to the storage device 625. The storage interface 620 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides the processor 605 with access to the storage device 625.

[0066] Memory areas 510 (shown in FIG. 3) and 610 may include, but are not limited to, random access memory (RAM) (e.g., dynamic RAM (DRAM) or static RAM (SRAM)), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are merely examples and are not limiting as to the types of memory available for storage of computer programs.

[0067] The computer systems and computer-implemented methods discussed herein may include additional, fewer, or alternative operations and / or functions (including those discussed elsewhere herein). The computer systems may include or be implemented via computer-executable instructions stored on a non-transitory computer-readable medium. The methods may be implemented via one or more local, remote, cloud-based processors, transceivers, servers, and / or sensors (e.g., processors, transceivers, servers, and / or sensors installed in an automobile or mobile device, or associated with a smart infrastructure or remote server), and / or via computer-executable instructions stored on a non-transitory computer-readable medium.

[0068] In some aspects, the computing device is configured to implement machine learning such that the computing device "learns" to analyze, organize, and / or process data without being explicitly programmed. Machine learning may be implemented via machine learning (ML) methods and algorithms. In one aspect, a machine learning (ML) module is configured to implement ML methods and algorithms. In some aspects, the ML methods and algorithms are applied to data inputs to generate machine learning (ML) outputs. The data inputs may include, but are not limited to, images or frames of video, object characteristics, and object classifications. The data inputs may further include: sensor data, image data, video data, telematics data, authentication data, authorization data, security data, mobile device data, geolocation information, transaction data, personal identification data, financial data, usage data, weather pattern data, "big data" sets, and / or user preference data. The ML outputs may include, but are not limited to: tracked shape output, object classification, motion type classification, diagnosis based on object motion, object motion analysis, and trained model parameters. ML outputs may further include: speech recognition, image or video recognition, functional connectivity data, medical diagnostics, statistical or financial models, autonomous vehicle decision-making models, robot behavior modeling, fraud detection analysis, user recommendations and personalized optimization, gaming AI, skill acquisition, targeted marketing, big data visualization, weather forecasts, and / or information extracted about a portion of a computing device, a user, a home, a vehicle, or a transaction. In some aspects, the data input may include a particular ML output.

[0069] In some aspects, at least one of a number of ML methods and algorithms may be applied, including, but not limited to, linear or logistic regression, example-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines. In various aspects, the ML methods and algorithms implemented relate to one of a number of classifications of machine learning (e.g., supervised learning, unsupervised learning, and reinforcement learning).

[0070] In one aspect, ML methods and algorithms relate to supervised learning, which involves identifying patterns in existing data to make predictions about subsequently received data. Specifically, ML methods and algorithms for supervised learning are "trained" through training data that includes input examples and associated output examples. Based on the training data, the ML methods and algorithms can generate a predictive function that maps outputs to inputs and utilize the predictive function to generate ML outputs based on data inputs. The input and output examples of the training data can include any of the data inputs or ML outputs described above. For example, an ML module can receive training data that includes customer identification and geographic information and associated customer categories, generate a model that maps customer categories to customer identification and geographic information, and generate an ML output that includes customer categories for subsequently received data inputs that include customer identification and geographic information.

[0071] In another aspect, the ML method and algorithm relate to unsupervised learning, which involves finding meaningful relationships in unorganized data. Unlike supervised learning, unsupervised learning does not involve user-driven training based on input examples with associated outputs. Rather, in unsupervised learning, unlabeled data, which may be any combination of data inputs and / or ML outputs as described above, is organized according to algorithmically determined relationships. In one aspect, an ML module accepts unlabeled data including customer purchase information, customer mobile device information, and customer geolocation information, and the ML module uses unsupervised learning methods (e.g., "clustering") to identify patterns and organize the unlabeled data into meaningful groups. The newly organized data can be used, for example, to extract further information about customer consumption habits.

[0072] In yet another aspect, the ML method and algorithm is related to reinforcement learning, which includes optimizing an output based on feedback from a reward signal. Specifically, the ML method and algorithm for reinforcement learning can accept a user-defined reward signal definition, accept a data input, utilize a decision model to generate an ML output based on the data input, accept a reward signal based on the reward signal definition and the ML output, and modify the decision model to accept a stronger reward signal for the subsequently generated ML output. The reward signal definition can be based on either the data input or the ML output. In one aspect, the ML module implements reinforcement learning in a user recommendation application. The ML module can utilize a decision model to generate a ranked list of choices based on user information received from a user, and can further accept selection data based on a user selection of one of the ranked choices. A reward signal can be generated based on a comparison of the selection data and a ranking of the selected choice. The ML module can update the decision model so that the subsequently generated ranking more accurately predicts user selection.

[0073] As will be understood based on the foregoing specification, the above aspects of the present disclosure may be implemented using computer programming or engineering techniques, including computer software, firmware, hardware, or any combination or subset thereof. Any such resulting program (which has computer readable code means) may be embodied or provided in one or more computer readable media, thereby creating a computer program product, i.e., an article of manufacture, according to the discussed aspects of the present disclosure. The computer readable medium may be, for example, but not limited to, a fixed (hard) drive, a diskette, an optical disk, a magnetic tape, a semiconductor memory (e.g., a read-only memory (ROM), and / or any transmission / reception medium (e.g., the Internet or other communications network or link). An article of manufacture containing computer code may be created and / or used by executing the code directly from one medium, by copying the code from one medium to another, or by transmitting the code over a network.

[0074] These computer programs (also known as programs, software, software applications, "apps", or codes) contain machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" each refer to any computer program product, apparatus, and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that accepts machine instructions as a machine-readable signal. However, "machine-readable medium" and "computer-readable medium" do not include transitory signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0075] As used herein, a processor may include any programmable system, including systems that use microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), logic circuits, and any other circuitry or processor capable of performing the functions described herein. The above examples are illustrative only and are not intended to limit in any way the definition and / or meaning of the term "processor."

[0076] As used herein, the terms "software" and "firmware" are used interchangeably and include any computer program stored in memory (including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory) for execution by a processor. The above memory types are exemplary only and are not limiting as to the types of memory usable for storage of computer programs.

[0077] In one aspect, a computer program is provided, the program being embodied in a computer readable medium. In one aspect, the system runs on a single computer system without requiring connection to a server computer. In a further aspect, the system runs in a Windows environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Washington). In yet another aspect, the system runs in a mainframe environment and a UNIX server environment (UNIX is a registered trademark of X / Open Company Limited, located in Reading, Berkshire, United Kingdom). The application is flexible and designed to run in a variety of different environments without compromising any key functionality.

[0078] In some aspects, the system includes multiple components disposed in multiple computing devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium. The system and process are not limited to the specific aspects described herein. Furthermore, each system and each process component may be implemented independently and separately from other components and processes described herein. Each component and process may also be used in combination with other assembly packages and processes. This aspect may enhance the functionality and functioning of a computer and / or computer system.

[0079] The method and algorithm of the present invention can be encapsulated in a controller or processor.Furthermore, the method and algorithm of the present invention can be embodied as a computer-implemented method or a method for performing such a computer-implemented method, and can also be embodied in the form of a tangible or non-transitory computer-readable storage medium that contains a computer program or other machine-readable instructions (herein "computer program"), where when said computer program is loaded into and / or executed by a computer or other processor (herein "computer"), said computer becomes an apparatus for performing said method.Storage media for containing such computer programs include, for example, floppy disks and diskettes, compact disks (CD)-ROMs (whether or not writable), DVD digital disks, RAM and ROM memory, computer hard drives and backup drives, external hard drives, "thumb" drives, and any other storage medium that can be read by a computer. The method may also be embodied in the form of a computer program, for example, whether stored in a storage medium, transmitted via an electrically conductive medium (e.g., an electrical conductor, optical fiber or other optical conductor), or by electromagnetic radiation, where when the computer program is written to and / or executed by a computer, the computer becomes an apparatus for carrying out the method. The method may be implemented in a general-purpose microprocessor or a digital processor specifically configured to carry out the process. When a general-purpose microprocessor is used, the computer program code configures the circuitry of the microprocessor to create a particular logic circuit arrangement. A computer-readable storage medium includes a medium readable by the computer itself or by another machine that reads computer instructions to provide those instructions to a computer to control its operation.Such machines may include, for example, machines for reading the storage media described above.

[0080] The compositions and methods described herein that utilize molecular biology protocols may follow a variety of standard techniques known in the art (e.g., Sambrook and Russel (2006) Condensed Protocols from Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN-10: 0879697717; Ausubel et al. (2002) Short Protocols in Molecular Biology, 5th ed., Current Protocols, ISBN-10: 0471250929; Sambrook and Russel (2001) Molecular Cloning: A Laboratory Manual, 3d ed., Cold Spring Harbor Laboratory Press, ISBN-10: 0879695773; Elhai, J. and Wolk, CP 1988. Methods in Enzymology 167, 747-754; Studier (2005) Protein Expr Purif. 41(1), 207-234; Gellissen, ed. (2005) Production of Recombinant Proteins: Novel Microbial and Eukaryotic Expression Systems, Wiley-VCH, ISBN-10: 3527310363; Baneyx (2004) Protein Expression Technologies, Taylor & Francis, ISBN-10: 0954523253).

[0081] The definitions and methods set forth herein are provided to better define the present disclosure and to guide those skilled in the art in the practice of the present disclosure. Unless otherwise noted, terms should be understood according to conventional usage by those of ordinary skill in the relevant art.

[0082] In some embodiments, numbers expressing quantities of ingredients, properties (e.g., molecular weight, reaction conditions, etc.) used to describe and claim certain embodiments of the present disclosure should be understood to be modified in some cases by the term "about". In some embodiments, the term "about" is used to indicate that a value includes the average standard deviation for the device or method being used to determine the value. In some embodiments, the numerical parameters set forth in the description and appended claims are approximations that may vary depending on the desired properties sought to be obtained by a particular embodiment. In some embodiments, such numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the present disclosure are approximations, the numerical values ​​set forth in the specific examples are reported as precisely as practicable. The numerical values ​​set forth in some embodiments of the present disclosure may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. The recitation of ranges of values ​​herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated herein as if it were individually set forth herein. The recitation of discrete values ​​is understood to include ranges between the values.

[0083] In some embodiments, the terms "a," "an," and "the," as well as references to the singular as used in the context of describing particular embodiments (particularly in the context of certain portions of the claims below), can be construed to cover both the singular and the plural, unless specifically noted otherwise. In some embodiments, the term "or" is used to mean "and / or" as used herein, including in the claims, unless expressly indicated to refer to alternatives only or where the alternatives are not mutually exclusive.

[0084] The terms "comprise", "have" and "include" are open-ended linking verbs. Any form or tense of one or more of these verbs (e.g., "comprises", "comprising", "has", "having", "includes" and "including") are also open-ended. For example, any method that "comprises", "has" or "includes" one or more steps is not limited to having only those one or more steps, but may also cover other unrecited steps. Similarly, any composition or device that "comprises", "has" or "includes" one or more features is not limited to having only those one or more features, but may also cover other unrecited features.

[0085] All methods described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples or exemplary language (e.g., "such as") provided herein with respect to specific embodiments is intended only to better elucidate the disclosure and does not impose limitations on the scope of the disclosure as otherwise claimed. No language in this specification should be construed as indicating any non-claimed element essential to the practice of the disclosure.

[0086] Grouping of alternative elements or embodiments of the disclosure disclosed herein should not be interpreted as limiting. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group may be included in or deleted from a group for convenience or patentability reasons. When any such inclusion or deletion occurs, the specification is deemed to include the group as modified to the extent that it satisfies all Markush group descriptions used in the appended claims.

[0087] All publications, patents, patent applications, and other references cited in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication, patent, patent application, or other reference was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. The citation of a reference herein shall not be construed as an admission that such is prior art to the present disclosure.

[0088] Although the present disclosure has been described in detail, it is apparent that modifications, variations, and equivalent embodiments are possible without departing from the scope of the present disclosure as defined in the appended claims. Moreover, it should be understood that all examples in the present disclosure are provided as non-limiting examples. EXAMPLES

[0089] The following non-limiting examples are provided to further illustrate the present disclosure. It should be understood by those skilled in the art that the techniques disclosed in the following examples represent approaches that the inventors have found to work well in implementing the present disclosure, and therefore may be considered to constitute examples of modes for its implementation. However, those skilled in the art should understand in light of the present disclosure that many changes can be made in the specific embodiments disclosed and still obtain the same or similar results without departing from the spirit and scope of the present disclosure.

[0090] Example 1: Fragment counting method to determine cell state composition from serum cfDNA This example describes a method for determining cell state composition from a biological sample using the fragment counting method described herein. This example also discloses prediction of treatment response and immune-related adverse event characteristics based on the cell state composition obtained using the disclosed system and method.

[0091] An example of a bin plot generated by the read-counting method of the present disclosure (showing the 24 cell types / states identified by the method) can be found in Figure 5. To determine whether the read-counting method can predict immunotherapy response in melanoma patients, the melanoma tumor DNA fraction (Figure 6A) and tumor infiltrating leukocyte (TIL) DNA fraction (Figure 7A) in cell-free DNA samples from plasma derived from melanoma patients with and without sustained clinical benefit were compared, and the sensitivity and specificity of the method's ability to predict response was characterized (Figures 6B and 7B), demonstrating its ability to predict response.

[0092] To determine whether the read-counting method can predict severe or symptomatic immune-related adverse effects (irAEs) from immunotherapy in melanoma patients, CD4 TEM DNA fractions in cell-free DNA samples from plasma derived from melanoma patients with severe (Figure 8A) or symptomatic (Figure 9A) irAEs were compared with patients without severe or symptomatic irAEs. The sensitivity and specificity of the method's ability to predict severe (Figure 8B) or symptomatic (Figure 9B) irAEs from immunotherapy were characterized, showing that the method can predict severe and symptomatic irAEs using cell-free CD4 TEM fractions in cell-free DNA samples. It should be noted that cell-free CD4 TEM fractions were most significantly associated with severe irAEs (Figure 11). The method could also predict irAEs on a graded basis using CD4 cell-free DNA (Figure 10).

Claims

1. A method for determining the cellular state composition from a biological sample, wherein the method is (a) A step of providing the biological sample containing cell-free DNA, wherein the cell-free DNA comprises a plurality of cell-free DNA fragments; (b) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (c) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (d) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (e) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having a relevant reference methylation level most similar to the methylation level of the DNA fragment; (f) The step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (g) A method comprising the step of determining the cell state composition based on the read-count table.

2. The method according to claim 1, wherein the biological sample is a blood sample.

3. The method according to claim 1, wherein the reference methylation value arises from known cell types and known cellular states and optionally includes differently methylated CpGs derived from bacterial, viral, fungal, or eukaryotic parasitic DNA.

4. The method according to claim 1, wherein the cell-free DNA is derived from plasma.

5. The method according to claim 1, wherein the cell state composition comprises at least two cell types, and each cell type comprises at least two cell states.

6. The method according to claim 1, further comprising the step of inferring a melanoma tumor fraction, a tumor-infiltrating leukocyte fraction, CD4 TEM levels, and any combination thereof based on the cellular state composition.

7. A method for obtaining a melanoma tumor fraction as an indicator of the therapeutic response of a subject to be administered an immunotherapy treatment, wherein the method is: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the melanoma tumor fraction based on the cellular state composition. A method comprising the melanoma tumor fraction, wherein the melanoma tumor fraction exhibits a response to the immunotherapy treatment.

8. A method for obtaining a tumor-infiltrating leukocyte fraction as an indicator of the therapeutic response of a subject to be administered an immunotherapy treatment, wherein the method is: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the tumor-infiltrating leukocyte fraction based on the cellular state composition. A method comprising the tumor-infiltrating leukocyte fraction showing a response to the immunotherapy treatment.

9. A method for obtaining a CD4 TEM fraction as an indicator of the severity of immune-related adverse events in a subject to be administered an immunotherapy treatment, wherein the method comprises: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the CD4 TEM fraction based on the cellular state composition. A method comprising the CD4 TEM fraction, wherein the CD4 TEM fraction indicates the severity of immune-related adverse events.

10. A method for obtaining a CD4 TEM fraction as an indicator of symptomatic immune-related adverse events in a subject to be administered an immunotherapy treatment, wherein the method comprises: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the CD4 TEM fraction based on the cellular state composition. A method comprising the CD4 TEM fraction exhibiting the symptomatic immune-related adverse events.

11. A method for obtaining a CD4 TEM fraction as an indicator of the grade of immune-related adverse events in a subject to be administered an immunotherapy treatment, wherein the method comprises: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the CD4 TEM fraction based on the cellular state composition. A method comprising the CD4 TEM fraction, wherein the CD4 TEM fraction indicates the grade of the immune-related adverse event.

12. A method for obtaining at least one of a melanoma tumor fraction and a tumor-infiltrating leukocyte fraction as an indicator of the therapeutic response of a subject to be administered an immunotherapy treatment, and a CD4 TEM fraction as an indicator of a serious immune-related adverse event (irAE), symptomatic irAE, irAE grade, and any combination thereof of the subject, wherein the method is: (a) The cellular state composition of the subject is (i) A step of providing a biological sample containing cell-free DNA obtained from the subject, wherein the cell-free DNA contains a plurality of cell-free DNA fragments; (ii) A step of providing a ground truth reference table including multiple reference cell and tissue states and associated reference methylation levels; (iii) A step of identifying CpGs in each DNA fragment of the cell-free DNA and determining the methylation level associated with each DNA fragment; (iv) A step of comparing the methylation level of each DNA fragment with the reference methylation level associated with each cell and tissue state in the ground truth reference table; (v) Assigning each DNA fragment to the cell or tissue state from the ground truth reference table having the relevant reference methylation level most similar to the methylation level of the DNA fragment; (vi) the step of counting the number of DNA fragments assigned to each cell or tissue state in the ground truth reference table to generate a read-count table; and (vii) A step of determining the cell state composition based on the read-count table, The process determined by; and (b) A step of estimating the melanoma tumor fraction, tumor-infiltrating leukocyte fraction, and CD4 TEM fraction based on the cellular state composition. It includes, At least one of the melanoma tumor fraction and the tumor-infiltrating leukocyte fraction shows the response to the immunotherapy treatment; and The method wherein the CD4 TEM fraction represents the serious immune-related adverse event (irAE), the symptomatic irAE, the irAE grade, and any combination thereof.

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