Methods and systems for treating diseases using ATR / CHK1 signaling pathway inhibitors

Abstract

Methods and systems for determining and / or confirming a response to a checkpoint kinase 1 (Chk1) inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and methods of administering a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof to a subject determined to be responsive to such therapy are provided herein. The present disclosure, inter alia, solves this problem by identifying responder and non-responder populations to Chk1 inhibition therapy (e.g., prexasertib), Wee1 inhibition therapy, ATR inhibition therapy, or combinations thereof (e.g., prior to initiation of therapy) by defining a simple treatment benefit prediction biomarker signature (also referred to throughout as a response prediction signature) that effectively predicts treatment benefit.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 388,914, filed Jul. 13, 2022, the entire contents of which are incorporated herein by reference.

Background Art

[0002] Checkpoint kinases 1 and 2 (Chk1 and Chk2) are serine / threonine protein kinases that play important roles in the regulation of DNA replication and the DNA damage response. Hong et al., Clin. Cancer Res. 24(14):3263-3272 (2018); Smith et al., Exp. Rev. Mol. Med. 22(e2):1-15 (2020). In response to DNA replication stress or damage, as shown in FIGS. 1A and 1B, Chk1 and / or Chk2 are activated as part of the DNA damage and replication checkpoints. Prexasertib (also known as LY606368 and ACR-368) is a potent small molecule inhibitor of Chk1 and has been shown to regulate the DNA damage response, replication fork stability, origin firing, and mitotic entry. See Nikolaev and Yang, Improving the Therapeutic Ratio in Head and Neck Cancer, pp. 301-316, 6th ed., 2020; Lowry et al., Clin. Cancer Res. 23(15):4354-4363 (Aug. 2017); Blasius et al., Genome Biology, 12:R78 (1-14) (2011). Chk1 inhibitors, such as prexasertib, have been described as promising therapies for certain cancers, and some patients have achieved partial or complete responses. However, not all patients respond to Chk1 inhibitors (e.g., prexasertib), and the response rate is usually less than 15% in most cancer indications, with some notable exceptions. See Hong et al., Clin. Cancer Res. 24(14):3263-3272 (2018); Lee et al., A Study of Prexasertib (LY2606368) in Platinum-Resistant or Refractory Recurrent Ovarian Cancer, ClinicalTrials.gov identifier: NCT03414047, available at clinicaltrials.gov / ct2 / show / record / NCT03414047. Considerable efforts have been made to identify biomarkers that predict responsiveness to Chk1 inhibitors (e.g., prexasertib), particularly in cancer patients with tumors caused by HPV. See Hong et al., Clin. Cancer Res. 24(14):3263 - 3272(2018); Martinez et al., Proceedings of the American Association for Cancer Research Annual Meeting 2017;2017 Apr 1 - 5; Washington, DC. Philadelphia(PA): AACR; Cancer Res 2017;77(13 Suppl): Abstract nr 1778. To date, no successful example has been reported that provides a consistent and reliable way to identify which patients will respond to Chk1 inhibitor (e.g., prexasertib) therapy. Thus, the effectiveness of these therapies, e.g., prexasertib, can only be evaluated by treating patients and waiting for the results without knowing the likelihood of patient response. Due to the low response rates observed with such an approach, Chk1 inhibitors, including prexasertib, have not been approved to date. Therefore, there is a great need for a method to identify patients who will benefit from treatment with Chk1 inhibitors (including prexasertib) in order to ensure a sufficient overall response rate for approval. This would ensure that responders can readily receive this effective therapy and also avoid unnecessary overtreatment of non - responders who will not benefit from Chk1 inhibitor therapy.

Prior Art Documents

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Summary of the Invention

Means for Solving the Problems

[0004] This disclosure solves this problem by, among other things, defining a simple treatment benefit prediction biomarker signature (also referred to as a response prediction signature throughout) that effectively predicts treatment benefit by identifying responder and non-responder populations to Chk1 inhibition therapy (e.g., prexasertib), Wee1 inhibition therapy, ATR inhibition therapy, or combinations thereof (e.g., prior to the start of therapy).

[0005] In some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition, the method comprising administering to a subject in whom a tissue sample has been determined to exhibit a response prediction signature, a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, wherein the response prediction signature comprises: (a) a first biomarker score that is greater than or equal to a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; (b) a second biomarker score that is greater than or equal to a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and (c) (i) a third biomarker score that is greater than or equal to a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, or total cyclin A2;or (ii) a third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker comprises one or more of: total SCF (a complex of Skp1, Cullin, and an F-box protein), total FBXW7, or total p27, and methods are provided. Exemplary amino acid sequences of these aforementioned biomarkers can be found in the following section entitled "Sequences".;

[0006] In some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition, comprising combining a therapeutic agent that inhibits the expression or activity of a protein in the ATR / Chk1 signaling pathway, which is an inhibitor of DNA synthesis, with a DNA synthesis inhibitor, and administering the combination to a subject in whom a tissue sample has been determined not to exhibit a response prediction signature, wherein the response prediction signature comprises: (a) a first biomarker score that is equal to or greater than a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; (b) a second biomarker score that is equal to or greater than a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and (c) (i) a third biomarker score that is equal to or greater than a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, or total cyclin A2;or (ii) a third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker comprises one or more of the third biomarker scores, wherein the third biomarker is selected from total SCF, total FBXW7, or total p27. In some embodiments, the DNA synthesis inhibitor is gemcitabine.;

[0007] In some embodiments, the present disclosure provides a method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of protein expression or activity in the ATR / Chk1 signaling pathway, the method comprising: (a) receiving, by a processor of a computing device, data from a tissue sample of cells of the subject that provides the respective levels of one or more of a first, second, and third biomarker in the tissue sample; (b) receiving, by the processor, corresponding first, second, and third prediction thresholds for each of the first, second, and third biomarkers; (c) using the data received in step (a), calculating, by the processor, first, second, and third biomarker scores from the respective levels of the first, second, and third biomarkers; (d) using the data received in step (b) and the data calculated in step (c), comparing, by the processor, the first, second, and third biomarker scores to the corresponding first, second, and third prediction thresholds to determine the presence or absence of a response prediction signature in the tissue sample;(e) classifying, by a processor, the subject as responsive to the therapeutic agent based on the presence of a response prediction signature in the tissue sample or as non-responsive to the therapeutic agent based on the absence of a response prediction signature in the tissue sample, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27.;

[0008] In some embodiments, the response prediction signature comprises at least two of a first biomarker score, a second biomarker score, and a third biomarker score. In some embodiments, the response prediction signature comprises each of a first biomarker score, a second biomarker score, and a third biomarker score.

[0009] In some embodiments, at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, or third biomarker is positive. In some embodiments, each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, and third biomarker are positive. In some embodiments, the first prediction threshold is cells in which 3% or more of the first biomarker is positive. In some embodiments, the first prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the first biomarker is positive. In some embodiments, the second prediction threshold is cells in which 3% or more of the second biomarker is positive. In some embodiments, the second prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the second biomarker is positive. In some embodiments, the third prediction threshold is cells in which 3% or more of the third biomarker is positive. In some embodiments, the third prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the third biomarker is positive. In some embodiments, the first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, the third biomarker is total cyclin E1, and each of the first prediction threshold, the second prediction threshold, and the third prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of each of the first biomarker, the second biomarker, and the third biomarker is positive.

[0010] In some embodiments, at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample. In some embodiments, each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample.

[0011] In some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition, the method comprising administering to a subject in whom a tissue sample has been determined to exhibit a response prediction signature, a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, the response prediction signature comprising a composite score that is equal to or greater than a composite threshold, the composite score comprising: (a) a first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; (b) a second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and (c) a third biomarker score, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, and total cyclin A2, the composite score comprising two or more of the first, second, and third biomarker scores.

[0012] In some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition, the method comprising combining a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway with a DNA synthesis inhibitor and administering the combination to a subject in whom a tissue sample has been determined not to exhibit a response prediction signature, the response prediction signature comprising a composite score above a composite threshold, the composite score comprising: (a) a first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; (b) a second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and (c) a third biomarker score, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, and total cyclin A2, the composite score comprising two or more of the first, second, and third biomarker scores. In some embodiments, the DNA synthesis inhibitor is gemcitabine.

[0013] In some embodiments, the present disclosure provides a method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of protein expression or activity in the ATR / Chk1 signaling pathway, the method comprising: (a) receiving, by a processor of a computing device, data from a tissue sample of cells of a subject providing a respective level of one or more of a first, a second, and a third biomarker in the tissue sample; (b) receiving, by the processor, a composite threshold; (c) using the data received in step (a), calculating, by the processor, a first, a second, and a third biomarker score from the respective levels of the first, the second, and the third biomarker; (d) using the data calculated in step (c), calculating, by the processor, a composite score from the first, the second, and the third biomarker scores; and (e) using the data received in step (b) and the data calculated in step (d), comparing, by the processor, the composite score to the composite threshold to determine the presence or absence of a response prediction signature in the tissue sample.(f) classifying, by a processor, the subject as responsive to the therapeutic agent based on the presence of a response prediction signature in the tissue sample or non-responsive based on the absence of a response prediction signature in the tissue sample, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27.;

[0014] In some embodiments, the composite score includes each of a first biomarker score, a second biomarker score, and a third biomarker score. In some embodiments, the composite score is the sum of each of the biomarker scores, and optionally, the biomarker scores are weighted differently prior to the sum.

[0015] In some embodiments, at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, or third biomarker is positive. In some embodiments, each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, and third biomarker are positive.

[0016] In some embodiments, at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample. In some embodiments, each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample.

[0017] In some embodiments, the disease, disorder, or condition is related to Chk1, Wee1, ATR, or a combination thereof. In some embodiments, the disease, disorder, or condition is cancer. In some embodiments, the cancer is characterized by a solid tumor. In some embodiments, the cancer is selected from ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer (small cell and non-small cell), pancreatic cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, and sarcoma. In some embodiments, the cancer is ovarian cancer. In some embodiments, the ovarian cancer is high-grade serous ovarian cancer. In some embodiments, the disease, disorder, or condition is related to an oncogenic virus.

[0018] In some embodiments, the tissue sample is a tumor biopsy sample. In some embodiments, the tissue sample is tumor cells in a tumor biopsy sample. In some embodiments, at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of a first biomarker, a second biomarker, or a third biomarker in the nuclei of cells in the tissue sample. In some embodiments, each of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of a first biomarker, a second biomarker, or a third biomarker in the nuclei of cells in the tissue sample.

[0019] In some embodiments, the therapeutic agent is a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. In some embodiments, the therapeutic agent is a Chk1 inhibitor. In some embodiments, the Chk1 inhibitor is prexasertib, SRA-737, PHI-101, LY2880070, V158411, CASC-578, IMP10, SOL-578, or a pharmaceutically acceptable salt of any of the foregoing. In some embodiments, the Chk1 inhibitor is prexasertib, or a pharmaceutically acceptable salt thereof. In some embodiments, the Chk1 inhibitor is prexasertib (S)-lactate monohydrate. In some embodiments, the therapeutic agent is a Wee1 kinase inhibitor. In some embodiments, the Wee1 kinase inhibitor is selected from adavosertib (AZD1775, MK1775), asenocertib (Zn-C3), Debio0123, STC8123, ATRN-1051, NUV-569, and IMP7068. In some embodiments, the therapeutic agent is an ATR inhibitor. In some embodiments, the ATR inhibitor is selected from berzosertib (M6620, VX-970), galcitinib (M4344, VX-803), elimusertib (BAY1895344), ceralasertib (AZD6738), M1774, ATRN-119, and camonsertib (RP-3500).

[0020] In some embodiments, the first biomarker is selected from Ser296 phosphorylated Chk or nuclear Chk1. In some embodiments, the first biomarker is Ser296 phosphorylated Chk1. In some embodiments, the second biomarker is Ser473 phosphorylated Kap1. In some embodiments, the third biomarker is total cyclin E1. In some embodiments, the first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, and the third biomarker is total cyclin E1.

[0021] In some embodiments, the method of treating a disease, disorder, or condition further comprises administering a second anti-cancer agent to the subject.

[0022] In some embodiments, the present disclosure provides a system for identifying and / or classifying a subject having a disease, disorder, or condition as likely to respond or likely not to respond to a therapy before administering the therapy, the system comprising a processor and a memory having instructions thereon, the instructions, when executed by the processor, causing the processor to perform one or more steps of the methods described herein.

Brief Description of the Drawings

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[0054] For example, provided herein are systems and methods useful for administering a therapy to a subject determined to be responsive to the therapy, or alternatively, to a subject in whom a responsive state was confirmed prior to administering the therapy. In some embodiments, the present disclosure provides systems and methods for predicting and / or confirming a response to a therapy.

[0055] Definitions [AA][]Phosphorylated protein: As used herein, reference to a particular amino acid at a particular numbered position in a phosphorylated protein, e.g., Ser280 phosphorylated Chk1, refers to that particular numbered amino acid sequence of the canonical isoform of the wild-type protein as indicated by the referenced UniProt ID number. (Version: July 6, 2023) (See the section entitled “Sequences” herein). It will be understood by those skilled in the art that the corresponding position numbers of particular amino acids may vary in different or newly discovered isoforms of that protein. The position of the corresponding amino acid in such isoforms can be readily identified by comparing the complete sequence of the canonical wild-type form to the complete sequence of another isoform. Thus, as used herein, any reference to a particular amino acid at a particular numbered position in a protein will be understood by those skilled in the art to also include reference to the corresponding amino acid and its corresponding numbered position in any isoform of that protein.

[0056] About: As used herein, the term "about" refers to a value similar to the value referred to in the context. Generally, one of ordinary skill in the art who is familiar with the context will understand the appropriate degree of difference encompassed by "about" in that context. For example, in some embodiments, the term "about" may encompass a range of values within 25%, within 20%, within 19%, within 18%, within 17%, within 16%, within 15%, within 14%, within 13%, within 12%, within 11%, within 10%, within 9%, within 8%, within 7%, within 6%, within 5%, within 4%, within 3%, within 2%, within 1%, or less than the value referred to. In some embodiments, "about" refers to ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, ±1% of the value referred to.

[0057] Administration: As used herein, the term "administration" typically refers to administering a composition to a subject or system in order to achieve delivery of an agent, which is, for example, a composition, or is included in a composition, or is delivered by a composition in another manner.

[0058] Agent: As used herein, the term "agent" refers to an entity (such as a lipid, metal, nucleic acid, polypeptide, polysaccharide, small molecule, etc., or a complex, combination, mixture or system thereof [such as a cell, tissue, organism]), or a phenomenon (such as heat, electric current or electric field, magnetic force or magnetic field, etc.).

[0059] Analog: As used herein, the term "analog" means a substance that shares one or more specific structural features, elements, components, or parts with a reference substance. Typically, an "analog" exhibits significant structural similarity to the reference substance, such as sharing a core or consensus structure, but differs in a specific, defined manner. In some embodiments, an analog is a substance that can be generated from a reference substance, for example, by chemical manipulation of the reference substance. In some embodiments, an analog is a substance that can be generated through the execution of a synthetic process that is substantially the same as (e.g., shares multiple steps with) the synthetic process that generates the reference substance. In some embodiments, an analog is generated or can be generated by the execution of a synthetic process that is different from the synthetic process used to generate the reference substance.

[0060] Antagonist: As used herein, the term "antagonist" may refer to an agent or condition whose presence, level, degree, type, or form is associated with reducing the level or activity of a target. Antagonists can include agents of any chemical class, including, for example, small molecules, polypeptides, nucleic acids, carbohydrates, lipids, metals, and / or any other elements that exhibit the relevant inhibitory activity. In some embodiments, an antagonist may be a "direct antagonist" in that it binds directly to its target, and in some embodiments, an antagonist may be an "indirect antagonist" in that it exerts its effect by means other than direct binding to its target (e.g., by interacting with a regulator of the target such that the level or activity of the target is altered). In some embodiments, an "antagonist" may be referred to as an "inhibitor".

[0061] Antibody: As used herein, the term "antibody" refers to a polypeptide comprising the basic immunoglobulin sequence elements sufficient to effect specific binding to a particular target antigen. As is known in the art, a naturally produced intact antibody is a tetrameric substance of approximately 150 kD, composed of two identical heavy chain polypeptides (each approximately 50 kD) and two identical light chain polypeptides (each approximately 25 kD) that bind to each other to generally form what is referred to as a "Y-shaped" structure. Each heavy chain is composed of at least four domains (each approximately 110 amino acids in length): an amino-terminal variable (VH) domain (located at the tip of the Y structure), followed by three constant domains: CH1, CH2, and carboxy-terminal CH3 (located at the base of the stem of the Y). A short region known as the "switch" connects the heavy chain variable and constant regions. The "hinge" connects the CH2 and CH3 domains to the rest of the antibody. Two disulfide bonds in this hinge region connect the two heavy chain polypeptides to each other in an intact antibody. Each light chain is composed of two domains: an amino-terminal variable (VL) domain and a carboxy-terminal constant (CL) domain, which are separated from each other by another "switch". The intact antibody tetramer is composed of two heavy chain-light chain dimers, in which the heavy and light chains are linked to each other by one disulfide bond, and two other disulfide bonds connect the heavy chain hinge regions to each other, thereby connecting the dimers to each other and forming the tetramer. Also, naturally produced antibodies are typically glycosylated on the CH2 domain. Each domain in a natural antibody has a structure characterized by an "immunoglobulin fold" formed from two β-sheets (e.g., three-stranded, four-stranded, or five-stranded sheets) that pack against each other in a compressed antiparallel β-barrel. Each variable domain contains three hypervariable loops (CDR1, CDR2, and CDR3), known as "complementary determining regions", and four somewhat invariant "framework" regions (FR1, FR2, FR3, and FR4).When a natural antibody folds, the FR regions form β-sheets that confer a structural framework on the domain, and the CDR loop regions of both the heavy and light chains come together in three-dimensional space to form a single hypervariable antigen-binding site located at the tip of the Y-shaped structure. The Fc region of a naturally occurring antibody binds to elements of the complement system and also to receptors on effector cells, including, for example, effector cells that mediate cytotoxicity. As is known in the art, the affinity of the Fc region for Fc receptors and / or other binding properties can be modulated via glycosylation or other modifications. In some embodiments, the antibodies produced and / or utilized in accordance with the present invention include a glycosylated Fc domain, e.g., an Fc domain that has been modified or engineered such as by glycosylation. For purposes of the present invention, in certain embodiments, any polypeptide, or complex of polypeptides, that includes a sufficient immunoglobulin domain sequence found in a natural antibody, whether such polypeptide is produced naturally (e.g., produced by an animal in response to an antigen) or by recombinant manipulation, chemical synthesis, or other artificial systems or methodologies, may be referred to as, and / or be usable as, an "antibody." In some embodiments, the antibody is polyclonal. In some embodiments, the antibody is monoclonal. In some embodiments, the antibody has a constant region sequence characteristic of a murine, rabbit, primate, or human antibody. In some embodiments, the antibody sequence elements are humanized, primatized, chimeric, etc., as known in the art. Further, as used herein, the term "antibody" (unless otherwise stated or apparent from the context in a particular embodiment) may mean any construct or format known or developed in the art for exploiting the structural and functional characteristics of an antibody in alternative embodiments.For example, antibodies utilized in accordance with embodiments of the present invention include, but are not limited to, intact IgA, IgG, IgE, or IgM antibodies; bispecific or multispecific antibodies (e.g., Zybodies®); antibody fragments, such as Fab fragments, Fab’ fragments, F(ab’)2 fragments, Fd’ fragments, Fd fragments, and isolated CDRs or sets thereof; single-chain Fvs, polypeptide-Fc fusions, single-domain antibodies (e.g., shark single-domain antibodies such as IgNAR or fragments thereof), camelid antibodies, mask antibodies (e.g., Probodies®), Small Modular ImmunoPharmaceuticals (“SMIPs™”), single-chain diabodies or tandem diabodies (TandAb®), VHHs, Anticalins®, Nanobodies®, minibodies, BiTE®, ankyrin repeat proteins or DARPINs®, Avimers®, DARTs, TCR-like antibodies, Adnectins®, Affilins®, Trans-bodies®, Affibodies®, TrimerX®, MicroProteins, Fynomers®, Centyrins®, and KALBITOR®. In some embodiments, the antibody may lack covalent modifications (e.g., glycan attachment) that it would have if produced naturally. In some embodiments, the antibody can contain covalent modifications (e.g., glycan attachment, payload [e.g., detectable moiety, therapeutic moiety, catalytic moiety, etc.], or other pendant groups [e.g., polyethylene glycol, etc.]).

[0062] Relevance: When this term is used in this specification, two events or entities are "relevant" to each other when the presence, level, degree, type, and / or form of one correlates with the presence, level, degree, type, and / or form of the other. For example, a particular entity (e.g., a polypeptide, gene signature, metabolite, microorganism, etc.) is considered relevant to a particular disease, disorder, or condition if its presence, level, and / or form correlates with the occurrence and / or susceptibility to the disease, disorder, or condition (e.g., across a relevant population). In some embodiments, two or more entities are physically "related" to each other if they interact directly or indirectly, and as a result, are physically proximate to and / or maintain a proximate state to each other. In some embodiments, two or more entities that are physically related to each other are covalently bonded to each other. In some embodiments, two or more entities that are physically related to each other are not covalently bonded to each other, but are non-covalently bonded, for example, by hydrogen bonds, van der Waals interactions, hydrophobic interactions, magnetism, and combinations thereof.

[0063] Biological sample: As used herein, the term "biological sample" generally refers to a sample obtained or derived from a biological source of interest (e.g., a tissue or an organism or a cell culture) as described herein. In some embodiments, the source of interest includes an organism such as an animal or a human. In some embodiments, the biological sample is or includes a biological tissue or fluid. In some embodiments, the biological sample is bone marrow; blood; blood cells; ascites; a tissue or fine needle biopsy sample; a cell-containing body fluid; free nucleic acid; sputum; saliva; urine; cerebrospinal fluid; peritoneal fluid; pleural fluid; feces; lymph fluid; gynecological body fluid; a skin swab; a vaginal swab; an oral swab; a nasal swab; a lavage or wash fluid; e.g., a tube lavage or bronchoalveolar lavage fluid; an aspirate; a scraping; a bone marrow sample; a tissue biopsy sample; a surgical sample; other body fluids; secretions; and / or excretions; and / or cells therefrom, etc., and may include them. In some embodiments, the biological sample is or includes cells obtained from an individual. In some embodiments, the obtained cells are or include cells of the individual from whom the sample was obtained. In some embodiments, the sample is a "primary sample" directly obtained from the source of interest by any suitable means. For example, in some embodiments, the primary biological sample is obtained by a method selected from the group consisting of biopsy (e.g., needle aspiration or tissue biopsy), surgery, collection of body fluids (e.g., blood, lymph fluid, feces, etc.). In some embodiments, the biological sample is a tumor biopsy sample. In some embodiments, as will be apparent from the context, the term "sample" refers to a preparation obtained by the treatment of a primary sample (e.g., by removing one or more components and / or by adding one or more agents). For example, filtration using a semipermeable membrane. Such a "processed sample" may include, for example, nucleic acids or proteins extracted from the sample or obtained by subjecting the primary sample to techniques such as amplification or reverse transcription of mRNA, separation and / or purification of specific components.

[0064] Biomarker: The term "biomarker" is used herein to refer to an entity (or a form thereof) whose presence or level correlates with a particular biological event or condition of interest, consistent with its usage in the art, such that it is regarded as a "marker" of that event or condition. To give only a few examples, in some embodiments, a biomarker may be or include a marker of a particular disease state or a marker of the likelihood that a particular disease, disorder, or condition may develop, occur, or recur. In some embodiments, a biomarker may be or include a marker for a particular disease or treatment outcome, or the likelihood thereof. Thus, in some embodiments, a biomarker has predictive ability for a biological event or condition of interest, in some embodiments, a biomarker has prognostic ability, and in some embodiments, a biomarker has diagnostic ability.

[0065] Combination therapy: As used herein, the term "combination therapy" refers to a clinical intervention in which a subject is simultaneously exposed to two or more treatment regimens (e.g., two or more therapeutic agents). In some embodiments, two or more treatment regimens may be administered simultaneously. In some embodiments, two or more treatment regimens may be administered sequentially (e.g., the first regimen is administered prior to the administration of any dose of the second regimen). In some embodiments, two or more treatment regimens are administered in an overlapping dosing regimen. In some embodiments, administration of combination therapy may include administering one or more therapeutic agents or therapies to a subject who is receiving another agent(s) or therapy. In some embodiments, combination therapy does not necessarily require administering the individual agents together (i.e., necessarily simultaneously) in a single composition. In some embodiments, two or more therapeutic agents or therapies of combination therapy are administered to the subject separately in separate compositions and / or at different times via, for example, separate routes of administration (e.g., one agent orally and another agent intravenously). In some embodiments, two or more therapeutic agents may be administered together in a combination composition, or a combination compound (e.g., as part of a single chemical complex or covalent entity), via the same route of administration and / or simultaneously.

[0066] Comparable: As used herein, the term "comparable" refers to two or more agents, entities, situations, sets of conditions, etc. that need not be identical to each other but are similar enough to enable a comparison between them, such that a person of ordinary skill in the art will understand that conclusions can be reasonably drawn based on the observed differences or similarities. In some embodiments, a comparable set of conditions, situation, individual, or population is characterized by a plurality of substantially identical features and one or a few different features. A person of ordinary skill in the art will understand, in any given context, how much identity is required for two or more such agents, entities, situations, sets of conditions, etc. to be considered comparable. For example, sets of situations, individuals, or populations are comparable to each other when, under different sets of circumstances, or differences in the results obtained using them or the phenomena observed, are caused by, or indicative of, changes in variable features and are characterized by a sufficient number and variety of substantially identical features to warrant a reasonable conclusion.

[0067] Corresponding: As used herein, the term "corresponding" refers to the relationship between two elements, events, or phenomena that share reasonably comparable sufficient characteristics such that the "corresponding" attributes are apparent. For example, in some embodiments, this term is used with respect to a compound or composition, and may indicate the position and / or identity of structural elements in the compound or composition through comparison with an appropriate reference compound or composition. For example, in some embodiments, a monomer residue in a polymer (e.g., an amino acid residue in a polypeptide or a nucleic acid residue in a polynucleotide) may be identified as "corresponding" to a residue in an appropriate reference polymer. For example, one of ordinary skill in the art will, for purposes of simplification, often designate residues in a polypeptide using a standard numbering system based on a reference related polypeptide, such that, for example, the amino acid "corresponding" to the residue at position 190 need not actually be the amino acid at position 190 of a particular amino acid chain, but rather will be understood to correspond to the residue at position 190 of the reference polypeptide; one of ordinary skill in the art will readily understand how to identify "corresponding" amino acids. For example, one of ordinary skill in the art will be aware of various sequence alignment methods, including, for example, software programs such as BLAST, CS-BLAST, CUSASW++, DIAMOND, FASTA, GGSEARCH / GLSEARCH, Genoogle, HMMER, HHpred / HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSI-BLAST, PSI-Search, ScalaBLAST, Sequilab, SAM, SSEARCH, SWAPHI, SWAPHI-LS, SWIMM, or SWIPE, which are available for identifying "corresponding" residues in polypeptides and / or nucleic acids, for example, in accordance with the present disclosure.

[0068] Dosage regimen or treatment regimen: Those skilled in the art will understand that the terms "dosage regimen" and "treatment regimen" can be used to mean a set (typically two or more) of unit doses that are administered individually to a subject, typically over time. In some embodiments, a given therapeutic agent may have a recommended dosage regimen that includes one or more doses. In some embodiments, the dosage regimen includes a plurality of doses, and each dose is temporally separated from the other doses. In some embodiments, the individual doses are separated from each other by periods of the same length. In some embodiments, the dosage regimen includes a plurality of doses and at least two different periods separating the individual doses. In some embodiments, all of the doses within the dosage regimen are of the same unit dose. In some embodiments, the different doses within the dosage regimen are of different amounts. In some embodiments, the dosage regimen includes a first dose at a first dosage, followed by one or more additional doses at a second dosage that is different from the first dosage. In some embodiments, the dosage regimen includes a first dose at a first dosage, followed by one or more additional doses at a second dosage that is the same as the first dosage. In some embodiments, the dosage regimen is correlated with a desired or beneficial result when administered within the relevant population (i.e., is a therapeutic dosage regimen).

[0069] Improved, increased, or decreased: As used herein, the terms "improved", "increased", or "decreased", or their grammatically equivalent comparative terms, indicate a relative value with respect to an equivalent reference measurement. For example, in some embodiments, an evaluation value achieved by a target agent may be "improved" compared to that obtained by an equivalent reference agent. Alternatively, or additionally, in some embodiments, an evaluation value achieved in a target subject or system is different under different conditions in the same subject or system (e.g., before and after an event such as administration of a target agent), or in a different equivalent subject (e.g., in an equivalent subject or system different from the target subject or system, in the presence of one or more indicators of a particular target disease, disorder, or condition, or before exposure to a condition or agent), compared to that obtained, may be "improved".

[0070] Non-responder: As used herein, the term "non-responder" refers to a patient or subject with poor improvement of clinical signs and symptoms after receiving a therapy (e.g., a Chk1 inhibitor, a Wee1 inhibitor, and / or an ATR inhibitor). In some embodiments, a non-responder may show an initial improvement in clinical signs and symptoms but shows a decrease in such improvement over time.

[0071] Pan or total: As used herein, the terms "pan" or "total" when referring to a specific biomarker (e.g., pan Wee1, nuclear pan Chk1, total cyclin E1, total cyclin A2) are used synonymously and refer to the total amount of all species of that biomarker (e.g., all possible modified (e.g., phosphorylated) forms, as well as unmodified (e.g., non-phosphorylated) forms) present intracellularly or at a specific location within the cell (e.g., the nucleus for "nuclear pan Chk1").

[0072] Patient or Subject: As used herein, the terms "patient" or "subject" refer to any organism to which the provided composition is administered, or may be administered, for purposes such as experimentation, diagnosis, prevention, cosmetic, and / or treatment. Typical patients or subjects include animals (e.g., mammals such as mice, rats, rabbits, non-human primates, and / or humans). In some embodiments, the patient is human. In some embodiments, the patient or subject suffers from, or is predisposed to, one or more disorders or conditions. In some embodiments, the patient or subject exhibits one or more symptoms of a disorder or condition. In some embodiments, the patient or subject is diagnosed with one or more disorders or conditions. In some embodiments, the patient or subject has received, or has previously received, a particular therapy for diagnosing and / or treating a disease, disorder, or condition.

[0073] Pharmaceutical Composition: As used herein, the term "pharmaceutical composition" refers to an active agent formulated with one or more pharmaceutically acceptable carriers. In some embodiments, the active agent is present in an appropriate unit dose (e.g., an amount that has been demonstrated to show a statistically significant probability of achieving a predetermined therapeutic effect when administered) for administration in a therapeutic regimen to a relevant subject, or a different equivalent subject (e.g., in the presence of one or more indicators of a particular disease, disorder or condition of interest, or in an equivalent subject or system different from the subject or system of interest, such as in the context of a previous exposure to a condition or agent). In some embodiments, the comparative terms refer to a statistically relevant difference (e.g., one of sufficient incidence and / or magnitude to achieve statistical relevance). One of ordinary skill in the art will know, or will be able to readily determine, the degree and / or incidence of difference necessary, or sufficient, to achieve such statistical significance in a given context.

[0074] Pharmaceutically acceptable: As used herein, the phrase "pharmaceutically acceptable" means a compound, material, composition, and / or dosage form that, within the scope of sound medical judgment, is suitable for use in contact with the tissues of humans and animals without excessive toxicity, irritation, allergic response, or other problems or complications and that has a reasonable benefit / risk ratio commensurate with such use.

[0075] Pharmaceutically acceptable salts: As used herein, the term "pharmaceutically acceptable salts" refers to salts of compounds that are suitable for use in a pharmaceutical context, i.e., within the scope of reasonable medical judgment, salts that do not cause excessive toxicity, irritation, allergic response, etc., and are suitable for use in contact with the tissues of humans and lower animals, having a reasonable benefit / risk ratio. Pharmaceutically acceptable salts are well known in the art. For example, S.M. Berge et al. describe pharmaceutically acceptable salts in detail in J. Pharmaceutical Sciences, 66:1-19 (1977). In some embodiments, pharmaceutically acceptable salts include, but are not limited to, non-toxic acid addition salts, which are formed with inorganic acids such as hydrochloric acid, hydrobromic acid, phosphoric acid, sulfuric acid, and perchloric acid, or organic acids such as acetic acid, maleic acid, tartaric acid, citric acid, succinic acid, or malonic acid, or are formed by other methods used in the art, such as ion exchange, and are salts of amino groups. In some embodiments, pharmaceutically acceptable salts include, but are not limited to, adipate, alginate, ascorbate, aspartate, benzenesulfonate, benzoate, bisulfate, borate, butyrate, camphorate, camphorsulfonate, citrate, cyclopentanepropionate, digluconate, dodecyl sulfate, ethanesulfonate, formate, fumarate, glucoheptonate, glycerophosphate, gluconate, hemisulfate, heptanoate, hexanoate, hydroiodide, 2-hydroxy-ethanesulfonate, lactobionate, lactate, laurate, lauryl sulfate, malate, maleate, malonate, methanesulfonate, 2-naphthalenesulfonate, nicotinate, nitrate, oleate, oxalate, palmitate, pamoate, pectinate, persulfate, 3-phenylpropionate, phosphate, picrate, pivalate, propionate, stearate, succinate, sulfate, tartrate, thiocyanate, p-toluenesulfonate, undecanoate, valerate, etc. Representative alkali metal salts or alkaline earth metal salts include sodium salts, lithium salts, potassium salts, calcium salts, magnesium salts, etc.In some embodiments, pharmaceutically acceptable salts include non-toxic ammonium, quaternary ammonium, and amine cations formed using counterions such as halides, hydroxides, carboxylic acids, sulfuric acid, phosphoric acid, nitric acid, alkyl having 1 to 6 carbon atoms, sulfonic acid, and arylsulfonic acid, where appropriate.

[0076] Prevent or prevention: As used herein, the terms “prevent” or “prevention,” when used in relation to the occurrence of a disease, disorder, and / or condition, refer to reducing the risk of developing the disease, disorder, and / or condition and / or delaying the manifestation of one or more features or symptoms of the disease, disorder, or condition. Prevention can be considered complete when the manifestation of the disease, disorder, or condition is delayed for a predetermined period.

[0077] Reference: As used herein, the term “reference” refers to a standard or control against which a comparison is made. For example, in some embodiments, a drug, animal, individual, population, sample, sequence, or value of interest is compared to a reference or control drug, animal, individual, population, sample, sequence, or value. In some embodiments, the reference or control is tested and / or determined substantially simultaneously with the test or determination of interest. In some embodiments, the reference or control is a historical reference or control and is optionally embodied in a tangible medium. Generally, as would be understood by those of ordinary skill in the art, the reference or control is determined or characterized under conditions or circumstances equivalent to those being evaluated. Those of ordinary skill in the art will understand when there is sufficient similarity to rely on and / or compare to a particular possible reference or control.

[0078] Responder: As used herein, the term “responder” refers to a patient or subject who shows improvement in clinical signs and symptoms after receiving a therapy (e.g., a Chk1 inhibitor, a Wee1 inhibitor, and / or an ATR inhibitor).

[0079] Small molecule: As used herein, the term "small molecule" means an organic and / or inorganic compound of low molecular weight. Generally, a "small molecule" is a molecule having a size of less than about 5 kilodaltons (kD). In some embodiments, the small molecule is less than about 4 kD, 3 kD, about 2 kD, or about 1 kD. In some embodiments, the small molecule is less than about 800 daltons (D), about 600 D, about 500 D, about 400 D, about 300 D, about 200 D, or about 100 D. In some embodiments, the small molecule is less than about 2000 g / mol, less than about 1500 g / mol, less than about 1000 g / mol, less than about 800 g / mol, or less than about 500 g / mol. In some embodiments, the small molecule is not a polymer.

[0080] In some embodiments, the small molecule does not contain a polymer moiety. In some embodiments, the small molecule is not a protein or polypeptide and / or does not contain a protein or polypeptide (e.g., is not an oligopeptide or peptide). In some embodiments, the small molecule is not a polynucleotide and / or does not contain a polynucleotide (e.g., is not an oligonucleotide). In some embodiments, the small molecule is not a polysaccharide and / or does not contain a polysaccharide. For example, in some embodiments, the small molecule is not a glycoprotein, proteoglycan, glycolipid, etc. In some embodiments, the small molecule is not a lipid.

[0081] In some embodiments, the small molecule is a modulator (e.g., an inhibitor or activator). In some embodiments, the small molecule is biologically active. In some embodiments, the small molecule is detectable (e.g., contains at least one detectable moiety). In some embodiments, the small molecule is a therapeutic agent.

[0082] Those skilled in the art reading this disclosure will understand that the specific small molecule compounds described herein can be provided and / or utilized in any of various forms, such as crystalline forms (e.g., polymorphs, solvates, etc.), salt forms, protected forms, prodrug forms, ester forms, isomeric forms (e.g., optical isomers and / or structural isomers), isotopic forms, and the like.

[0083] Therapeutic agent: As used herein, the term "therapeutic agent" generally refers to any agent that, when administered to an organism, produces a desired pharmacological effect. In some embodiments, an agent is considered a therapeutic agent if it exhibits a statistically significant effect across an appropriate population. In some embodiments, the appropriate population can be a population of model organisms. In some embodiments, the appropriate population can be defined by various criteria such as a particular age group, sex, genetic background, existing clinical condition, and the like. In some embodiments, a therapeutic agent is a substance that can be used to alleviate, improve, reduce, inhibit, prevent, delay the onset of, reduce the severity of, and / or reduce the incidence of one or more symptoms or characteristics of a disease, disorder, and / or condition. In some embodiments, a "therapeutic agent" is an agent that has been approved or needs to be approved by a government agency before it can be commercially available for administration to humans. In some embodiments, a "therapeutic agent" is an agent that requires a medical prescription for administration to humans.

[0084] Therapeutically effective amount: As used herein, the term "therapeutically effective amount" refers to the amount of a substance (e.g., a therapeutic agent, composition, and / or formulation) that, when administered as part of a treatment regimen, induces a desired biological response. In some embodiments, a therapeutically effective amount of a substance is an amount sufficient to treat, diagnose, prevent, and / or delay the onset of a disease, disorder, and / or condition when administered to a subject having or susceptible to the disease, disorder, and / or condition. As will be appreciated by those skilled in the art, the effective amount of a substance may vary depending on factors such as the desired biological endpoint, the substance being delivered, the target cell or tissue, etc. For example, the effective amount of a compound in a formulation for treating a disease, disorder, and / or condition is an amount that alleviates, improves, reduces, inhibits, prevents, delays the onset of, decreases the severity of, and / or decreases the incidence of one or more symptoms or characteristics of the disease, disorder, and / or condition. In some embodiments, the therapeutically effective amount is administered as a single dose; in some embodiments, multiple unit doses are required to deliver the therapeutically effective amount.

[0085] Treat: As used herein, the terms "treat", "treatment", or "treating" refer to any method used to partially or completely alleviate, improve, reduce, inhibit, prevent, delay the onset of, decrease the severity of, and / or decrease the incidence of one or more symptoms or characteristics of a disease, disorder, and / or condition. Treatment may be performed on a subject that does not exhibit symptoms of the disease, disorder, and / or condition. In some embodiments, treatment may be administered to a subject exhibiting only early signs of a disease, disorder, and / or condition, for example, for the purpose of reducing the risk of developing a pathology associated with the disease, disorder, and / or condition.

[0086] Response prediction signature The present disclosure describes a response prediction signature that can distinguish a responder population from a non-responder population to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. In some embodiments, the response prediction signature as defined herein is confirmed by post hoc analysis of populations of responder and non-responder subjects administered the inhibitor.

[0087] In some embodiments, the provided response prediction signature is highly effective in predicting a response to one or more of Chk1 inhibitor therapy, Wee1 inhibitor therapy, and ATR inhibitor therapy in a subject. To date, previous attempts to identify a response prediction signature by a single biomarker have had limited success. In contrast, the provided techniques consistently and reliably determine whether a subject will respond to a particular therapy. In some embodiments, the provided techniques are useful for predicting a response to one or more of Chk1 inhibitor therapy, Wee1 inhibitor therapy, and ATR inhibitor therapy before any Chk1 inhibitor therapy, Wee1 inhibitor therapy, ATR inhibitor therapy, or any combination thereof is administered to a subject.

[0088] In some embodiments, the provided response prediction signature can identify responders and non-responders based on three or fewer biomarker components. As described herein, the methods and systems utilize the detection and quantification of first, second, and third biomarkers to determine whether a subject will respond to a Chk1 inhibitor (e.g., prexasertib), a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. The present disclosure includes the insight that no single biomarker alone is sufficient to predict a response, and it is the combination of biomarkers provided herein that provides accurate and consistent predictions regarding the response status of a given subject. Previous attempts to identify any single biomarker or combination of biomarkers to predict response with the accuracy of the set of biomarkers of the present disclosure for a Chk1 inhibitor have failed.

[0089] For example, Lee et al. reported that there was no clear association between the clinical response to prexasertib and the status of homologous recombination deficiency (HRD). Lee et al., Lancet Oncol., 19(2):207-215 (2018). Lee identified the possibility of a correlation between prexasertib activity and the amplification and / or overexpression of CCNE1, but the correlation was only a trend (amplification in 2 responders), and Lee showed that predictive validation was needed to confirm whether such a correlation persisted, and that such a correlation was not demonstrated in other clinical trials using prexasertib. Similarly, Hong et al. tracked tumor responses to gene markers, including a waterfall plot of the maximum change rate of tumor size from baseline. Hong identified loss-of-function mutations in two classes of genes in responsive subjects: (1) DDR pathway genes, such as BRCA1, BRCA2, MRE11A, and ATR, and (2) genes known to increase replication stress, such as E3 ubiquitin ligases targeting cyclin E1, such as FBXW7 and PARK2, but no consistent or definitive markers were identified. Hong et al., Clin. Cancer Res. 24(14):3263-3272 (2018). Blosser et al. found that "none of the identified variants passed the statistical cutoff" in an attempt to "identify molecular traits that track prexasertib response across a panel of pan-cancer cell line gene variants, including mutations, insertions / deletions, frameshift changes, splice variants, and copy number changes obtained from whole exome sequencing." Blosser et al., Oncotarget, 11:216-236 (2020). Others, including Byers et al. and Gatti-Mays et al., also attempted to identify biomarkers that accurately predict the response of subjects to prexasertib, but nothing was discovered in each study.See also Byers et al., Clin. Lung Cancer, 22(6):531-540(2021); Gatti-Mays, et al., The Oncologist, 25(6):479-e899(2020); Sen et al., Cancer Res., 77(14):3870-3884(2017) (which suggests that cMYC overexpression predicts sensitivity to Chk1 inhibition in SCLC).

[0090] However, the present disclosure overcomes the deficiencies of prior studies and provides a signature for predicting response to Chk1 inhibitors, such as prexasertib, with high accuracy. As described herein, such a signature (referred to as a response prediction signature) involves the analysis of specific biomarkers, such as specific proteins and / or modified (e.g., phosphorylated) proteins, which, when present in a specific amount in a tissue sample of a subject, indicate that the subject is responsive to treatment with a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. For just one such example, see FIG. 5. Here, the response prediction signature accurately predicts response to prexasertib in a blinded, pre-designed study when applied to pretreatment tumor biopsies from ovarian cancer patients in past trials using prexasertib, as evidenced by the substantially extended median survival of predicted responders receiving prexasertib compared to predicted non-responders.

[0091] In some embodiments, the response prediction signature involves detecting the presence (e.g., amount or level) of a first biomarker, a second biomarker, and / or a third biomarker. Once the presence of the first biomarker, the second biomarker, and / or the third biomarker is detected, the various amounts or levels of the first biomarker, the second biomarker, and / or the third biomarker are analyzed according to the methods described herein to provide a response prediction signature.

[0092] In some embodiments, the presence of first, second, and third biomarkers is determined and compared to first, second, and third prediction thresholds (as described herein), respectively. In some embodiments, at least one of the first, second, and third biomarkers is the presence of a phosphorylated protein biomarker or total protein. In some embodiments, at least one of the first, second, and third biomarkers is detected and quantified at a specific intracellular localization of the cell. In some embodiments, at least one of the first, second, and third biomarkers is detected and quantified in a specific region of interest within the tumor sample (e.g., within tumor cells only, within stromal regions only, etc.). In some embodiments, at least one of the first, second, and third biomarkers is detected and quantified within the nucleus of the cell. In some embodiments, each of the first, second, and third biomarkers is detected and quantified within the nucleus of the cell.

[0093] In some embodiments, the response prediction signature comprises the presence of only two of the first biomarker, the second biomarker, and the third biomarker. The presence of these two biomarkers is determined and compared to their corresponding prediction thresholds. In some of these embodiments, the response prediction signature comprises the presence of the first biomarker and the second biomarker. In some of these embodiments, the response prediction signature comprises the presence of the second biomarker and the third biomarker. In some of these embodiments, the response prediction signature comprises the presence of the first biomarker and the third biomarker. In some embodiments, at least one of the two biomarkers is detected and quantified at a specific intracellular localization of the cell. In some embodiments, at least one of the two biomarkers is detected and quantified in a specific region of the tumor sample (e.g., within tumor cells only, within stromal regions only, etc.). In some embodiments, at least one of the two biomarkers is detected and quantified within the nucleus of the cell. In some embodiments, each of the two biomarkers is detected and quantified within the nucleus of the cell.

[0094] Each of the first, second, and third biomarkers, and their respective assays for use in a response prediction signature are described below.

[0095] The first biomarker Chk1 activity is controlled by the upstream kinase ATR, and the ATR-Chk1 pathway controls genomic integrity by the cell cycle checkpoint mechanism. Upon DNA damage, ATR is activated and phosphorylates Chk1 at serines 317 and 345, leading to an increase in the catalytic activity of Chk1. The increased activity promotes autophosphorylation at serine 296. Phosphorylation at this site enables the release of Chk1 from the damage site and the transmission of the DDR signal to its downstream targets dispersed throughout the nucleus, including, for example, Wee1. The output of this signaling cascade is cell division cycle arrest and DNA damage repair. Normal cells and cancer cells depend on a functional ATR-Chk1 pathway for effective DDR. However, due to several common perturbations in cancer cells, including loss of p53 function and high replication stress, cancer cells rely more heavily on this pathway to survive / resolve DNA damage. Chk1 inhibition promotes the accumulation of replication-induced DNA damage and premature entry into mitosis.

[0096] In some embodiments, the first biomarker is the phosphorylated form of Chk1. In some embodiments, the first biomarker is Chk1 phosphorylated at Ser280, Ser296, Ser317, or Ser345. In some embodiments, the first biomarker is the phosphorylated form of Chk2. In some embodiments, the first biomarker is Chk2 phosphorylated at Thr68 or Se516. In some embodiments, the first biomarker is the presence of total protein (e.g., nuclear Chk1 (i.e., nuclear pan Chk1)). In some embodiments, the first biomarker is selected from one or more of Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2. In some embodiments, the first biomarker is selected from one or more of Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, and Thr383 phosphorylated Chk2. In some embodiments, the first biomarker is selected from one or more of Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, and Thr383 phosphorylated Chk2. In some embodiments, the first biomarker is Ser280 phosphorylated Chk1. In some embodiments, the first biomarker is Ser296 phosphorylated Chk1. In some embodiments, the first biomarker is Ser317 phosphorylated Chk1. In some embodiments, the first biomarker is Ser345 phosphorylated Chk1. In some embodiments, the first biomarker is nuclear Chk1. In some embodiments, the first biomarker is Thr68 phosphorylated Chk2. In some embodiments, the first biomarker is Ser516 phosphorylated Chk2.In some embodiments, the first biomarker is Thr383 phosphorylated Chk2. In some embodiments, the first biomarker is Thr387 phosphorylated Chk2.

[0097] The second biomarker Kap1, also known as TRIM28, is a large multi-domain protein involved in many cellular functions, including transcriptional repression and DDR. Kap1 interacts with other proteins via its structural domains to promote the formation of condensed heterochromatin and transcriptional silencing. The central part of Kap1 contains a region that mediates interaction with heterochromatin protein 1 (HP1). In response to DNA damage, Kap1 is phosphorylated at multiple sites, including serine 473, which is located near the HP1 binding site. This phosphorylation event disrupts the interaction between Kap1 and HP1, leading to a slightly decondensed DNA structure and, importantly, providing accessibility to the repair machinery for damaged DNA. Depending on the type of damage, either Chk1 or Chk2 can phosphorylate serine 473. Kap1 is also phosphorylated at serine 824 upon DNA damage, interfering with its interaction with other types of repressor proteins.

[0098] In some embodiments, the second biomarker is a phosphorylated form of Kap1, Treslin, claspin, FAM122A, Wee1, CDC25B, or FOXM1. In some embodiments, the second biomarker is a phosphorylated form of Kap1. In some embodiments, the second biomarker is the presence of total protein (e.g., Wee1 (i.e., total / pan Wee1)). In some embodiments, the second biomarker is selected from one or more of phosphorylated Ser473 Kap1, phosphorylated Ser865 Treslin, phosphorylated Ser743 TLK1, phosphorylated Ser612 RB1, phosphorylated Ser1310 MYBBP1A, phosphorylated Ser508 SETMAR, phosphorylated Thr2252 SRRM2, phosphorylated Ser230 CDC25B, phosphorylated Ser950 claspin, phosphorylated Ser37 FAM122A, phosphorylated Ser642 Wee1, total Wee1, phosphorylated Ser151 CDC25B, phosphorylated Ser280 CDC25B, phosphorylated Ser481 FOXM1, and phosphorylated Ser704 FOXM1. In some embodiments, the second biomarker is selected from one or more of phosphorylated Ser473 Kap1, phosphorylated Ser642 Wee1, pan Wee1, phosphorylated Ser865 Treslin, phosphorylated Ser743 TLK1, phosphorylated Ser1310 MYBBP1A, phosphorylated Ser508 SETMAR, phosphorylated Thr2252 SRRM2, phosphorylated Ser230 CDC25B, phosphorylated Ser950 claspin, and phosphorylated Ser280 CDC25B. In some embodiments, the second biomarker is selected from one or more of phosphorylated Ser473 Kap1, phosphorylated Ser642 Wee1, phosphorylated Ser865 Treslin, phosphorylated Ser1310 MYBBP1A, phosphorylated Ser230 CDC25B, and phosphorylated Ser950 claspin. In some embodiments, the second biomarker is phosphorylated Ser473 Kap1. In some embodiments, the second biomarker is phosphorylated Ser865 Treslin. In some embodiments, the second biomarker is phosphorylated Ser743 TLK1. In some embodiments, the second biomarker is phosphorylated Ser612 RB1.In some embodiments, the second biomarker is phosphorylated MYBBP1A at Ser1310. In some embodiments, the second biomarker is phosphorylated SETMAR at Ser508. In some embodiments, the second biomarker is phosphorylated SRRM2 at Thr2252. In some embodiments, the second biomarker is phosphorylated CDC25B at Ser230. In some embodiments, the second biomarker is phosphorylated claspin at Ser950. In some embodiments, the second biomarker is phosphorylated FAM122A at Ser37. In some embodiments, the second biomarker is phosphorylated Wee1 at Ser642. In some embodiments, the second biomarker is total Wee1. In some embodiments, the second biomarker is phosphorylated CDC25B at Ser151. In some embodiments, the second biomarker is phosphorylated CDC25B at Ser280. In some embodiments, the second biomarker is phosphorylated FOXM1 at Ser481. In some embodiments, the second biomarker is phosphorylated FOXM1 at Ser704.

[0099] The third biomarker Cyclin E1 is an important regulator of DNA replication initiation (G1 / S transition) and a major driver of replication stress in cancer. By activating cyclin-dependent kinase 2 (CDK2), the cyclin E-CDK2 complex promotes cell cycle progression through several mechanisms, including firing of DNA replication origins. Dysregulation of cyclin E1 expression is common in many cancer types and can be caused by genomic amplification, transcriptional upregulation, or interference with proteolysis. Upregulated cyclin E1 expression leads to enhanced CDK2 activity and increased DNA replication beyond the cell's capacity, leading to replication stress and genomic instability. Thus, such cancer cells constantly require repair of replication-induced DNA damage to survive, which makes them dependent on active ATR-Chk1 signaling. The SCF complex and FBXW7 regulate the degradation of cyclin E1, and p27 regulates cyclin E1 / CDK2 activity. Thus, low expression of one or more of SCF, FBXW7, and / or p27 may lead to upregulation of cyclin E1 or high activity of the cyclin E1 / CDK2 complex. See, for example, Koepp et al., Science 294(5540):173-7(2001); and Sheaff et al., Genes Dev 11(11):1464-78(1997). Thereby, when below a third predictive threshold, it is demonstrated that any one of these three can be used as a biomarker (as a third biomarker) to determine the sensitivity of a subject to a therapeutic agent that is an inhibitor of protein expression or activity in the ATR / Chk1 signaling pathway.

[0100] In some embodiments, the third biomarker is a phosphorylated form of nucleophosmin, CDC6, or Treslin. In some embodiments, the third biomarker is the presence of a total protein (e.g., cyclin E1, CDC6, Cks1, Cks2, or cyclin A2). In some embodiments, the third biomarker is selected from one or more of total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, nuclear CDC6 (i.e., pan nuclear CDC6), Ser1000 phosphorylated Treslin, total Cks1 (i.e., pan Cks1), total Cks2, or total cyclin A2. In some embodiments, the third biomarker is selected from one or more of total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, total Cks1, total Cks2, and total cyclin A2. In some embodiments, the third biomarker is selected from one or more of total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, and total cyclin A2. In some embodiments, the third biomarker is selected from one or more of total cyclin E1 and total cyclin A2. In some embodiments, the third biomarker is total cyclin E1. In some embodiments, the third biomarker is Ser100 phosphorylated cyclin E1. In some embodiments, the third biomarker is Ser103 phosphorylated cyclin E1. In some embodiments, the third biomarker is Ser387 phosphorylated cyclin E1.In some embodiments, the third biomarker is Thr395 phosphorylated cyclin E1. In some embodiments, the third biomarker is Thr199 phosphorylated nucleophosmin. In some embodiments, the third biomarker is Ser54 phosphorylated CDC6. In some embodiments, the third biomarker is Ser74 phosphorylated CDC6. In some embodiments, the third biomarker is pan nuclear CDC6. In some embodiments, the third biomarker is Ser1000 phosphorylated treslin. In some embodiments, the third biomarker is total Cks1. In some embodiments, the third biomarker is total Cks2. In some embodiments, the third biomarker is total cyclin A2.

[0101] In some embodiments, the third biomarker is selected from total SCF, total FBXW7, or total p27. In some embodiments, the third biomarker is total SCF. In some embodiments, the third biomarker is total FBXW7. In some embodiments, the third biomarker is total p27.

[0102] In some embodiments, the third biomarker is not total cyclin E1. In some embodiments, the third biomarker is not Ser100 phosphorylated cyclin E1. In some embodiments, the third biomarker is not Ser103 phosphorylated cyclin E1. In some embodiments, the third biomarker is not Ser387 phosphorylated cyclin E1. In some embodiments, the third biomarker is not Thr395 phosphorylated cyclin E1.

[0103] In some embodiments, if the response prediction signature includes the presence of only a third biomarker (not a first or second biomarker), then the third biomarker is not total cyclin E1. In some aspects of these embodiments, the third biomarker is not Ser100 phosphorylated cyclin E1. In some aspects of these embodiments, the third biomarker is not Ser103 phosphorylated cyclin E1. In some aspects of these embodiments, the third biomarker is not Ser387 phosphorylated cyclin E1. In some aspects of these embodiments, the third biomarker is not Thr395 phosphorylated cyclin E1.

[0104] Combination of biomarkers In some embodiments, the first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, and the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan-nuclear CDC6, Ser1000 phosphorylated Treslin, total Cks1, total Cks2, and total cyclin A2.

[0105] In some embodiments, the first biomarker is Ser296 phosphorylated Chk1, the third biomarker is total cyclin E1, and the second biomarker is selected from Ser473 phosphorylated Kap1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser642 phosphorylated Wee1, total Wee1, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1.

[0106] In some embodiments, the second biomarker is Ser473 phosphorylated Kap1, the third biomarker is total cyclin E1, and the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2.

[0107] In some embodiments, the combination of the first, second, and third biomarkers is selected from any one of the combinations listed in Table A below.

Table 17-1

Table 17-2

[0108] In some embodiments, the first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, and the third biomarker is total cyclin E1.

[0109] Background Threshold As described herein, the detection of each biomarker (e.g., each of the first, second, or third biomarkers) utilized in the methods disclosed herein is analyzed by comparison to the background threshold of that biomarker. The background threshold represents, in some embodiments, the level (e.g., signal level) of a particular biomarker in cells (e.g., cell nuclei) that are considered background noise. If a cell in a tissue sample contains a biomarker that exceeds its background threshold, that cell is considered to have a “positive” for that particular biomarker. Each biomarker will have its own background threshold.

[0110] In some embodiments, the background threshold for a given biomarker is established based on the biomarker signal intensity observed in tumor samples from a plurality of subjects that were insufficiently responsive or non-responsive to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or combinations thereof for which the assay is intended. In some embodiments, the background threshold for a given biomarker is established based on the biomarker signal intensity observed in a plurality of tumor samples with the lowest overall signal intensity. In some embodiments, the background threshold for a given biomarker is established based on the biomarker signal intensity observed in a plurality of samples of normal (non-tumor) cells. In some embodiments, the background threshold is established by selecting some samples with negative biomarker expression and some samples with positive biomarker expression. Then, when applied to samples with negative biomarker expression, a background threshold can be established such that less than 1% of the cells in the sample (or other value deemed appropriate for scoring from biomarker-negative samples) exceed that background threshold. When the selected background threshold is applied to samples with positive biomarker expression, the number of cells above that background threshold should be significantly greater than in biomarker-negative cells and should correlate with the degree of biomarker expression. In some embodiments, the background threshold is the number of cells having a biomarker signal. In some embodiments, the background threshold is the overall signal intensity in the sample. In some embodiments, the background threshold is established as described in Example 7.

[0111] The cells in a sample (or the entire sample depending on the method used to establish the background threshold) determined to be above the background threshold are quantified and considered "positive".

[0112] Biomarker score As described herein, each biomarker (e.g., each of the first, second, or third biomarkers) utilized in the methods disclosed herein is analyzed to generate a score for that biomarker (e.g., a first, second, or third biomarker score). To avoid misunderstanding, when used herein, the first biomarker score refers to the biomarker score determined from the first biomarker as described herein, the second biomarker score refers to the biomarker score determined from the second biomarker as described herein, and the third biomarker score refers to the biomarker score determined from the third biomarker as described herein. In some embodiments, cells from a biological sample of a subject having a first, second, or third biomarker that exceeds a background threshold (i.e., cells that are positive for a particular biomarker, e.g., cells that are positive for a particular biomarker in the nucleus) are quantified. The amount of cells that are positive for each of the first, second, and / or third biomarkers (e.g., the percentage of positive cells relative to all cells in the sample) is quantified as the first, second, and / or third biomarker score, respectively.

[0113] In some embodiments, the first, second, and / or third biomarker score is the percentage of cells that are positive for the first, second, and / or third biomarker relative to all cells in the sample. Such biomarker scores are referred to herein as the "standard" scoring method. In some embodiments, the first biomarker score is the percentage of cells that are positive for the first biomarker relative to all cells in the sample. In some embodiments, the second biomarker score is the percentage of cells that are positive for the second biomarker relative to all cells in the sample. In some embodiments, the third biomarker score is the percentage of cells that are positive for the third biomarker relative to all cells in the sample.

[0114] The present disclosure recognizes that there are multiple ways to generate biomarker scores. In some embodiments, for each of the first, second, and / or third biomarkers, the intensity of the intracellular signal measured by the assay methods described herein (e.g., immunofluorescence assay) is quantified and used to generate the first, second, and / or third biomarker scores. Such biomarker scores are referred to herein as the "intensity" scoring method. In some embodiments, the intensity of the intracellular signal is the intensity of the signal at the intracellular location (e.g., the intensity of the signal within the cell nucleus). For example, in some embodiments, the biomarker score may be generated using the weighted intensity distribution of the cells in the sample. This is achieved by calculating the percentage bin distribution of the cell signal intensity. Each signal intensity bin is assigned a coefficient (weight) that depends on the biomarker signal intensity range of this bin (e.g., such that the bin with the lowest intensity has the lowest coefficient and the bin with the highest intensity has the highest coefficient). The percentage of cells within each bin is multiplied by the bin coefficient (weight). The final biomarker score is calculated as the sum of these multiplications. For example, the cell signal range of the biomarker is between 0 and 30, and the background threshold is 3. The cell signal range is divided into three bins, where bin 1 is from 3 to 10, bin 2 is from >10 to 20, and bin 3 is from >20 to 30. A coefficient of 0.1 is assigned to bin 1, a coefficient of 1 is assigned to bin 2, and a coefficient of 10 is assigned to bin 3. A sample containing 10% positive cells all belonging to bin 1 will have a score of 10%×0.1 = 1%. Another sample containing 3% positive cells all belonging to bin 3 will have a score of 3%×10 = 30%. Thus, a sample with few positive cells but a high signal intensity of the biomarker may have a higher biomarker score than a sample with many positive cells but a low signal intensity.In some embodiments, characteristics of the distribution of cell strength measurements may be used to generate scores such as quantiles, including the median, mean, mode, and quantiles of 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, and 99%.

[0115] In some embodiments, the first, second, and / or third biomarker scores are determined from the weighted intensity distribution of cells in a sample for the first, second, and / or third biomarker. In some embodiments, the first biomarker score is determined from the weighted intensity distribution of cells in a sample for the first biomarker. In some embodiments, the second biomarker score is determined from the weighted intensity distribution of cells in a sample for the second biomarker. In some embodiments, the third biomarker score is determined from the weighted intensity distribution of cells in a sample for the third biomarker.

[0116] In some embodiments, the weights used to generate biomarker scores from the weighted intensity distribution are specific to the biomarker. In some embodiments, the weights used to generate biomarker scores from the weighted intensity distribution are constants. In some embodiments, the weights used to generate biomarker scores from the weighted intensity distribution are determined using a function based on the signal intensity range of each bin.

[0117] In some embodiments, each of the first, second, and / or third biomarker scores is determined using the same method (e.g., a standard scoring method or an intensity scoring method). In some embodiments, different methods may be used to calculate each of the first, second, and / or third biomarker scores. For example, in some embodiments, each of the first and second biomarker scores is determined by a standard scoring method, and the third biomarker score is determined by an intensity scoring method. In some embodiments, each of the first and third biomarker scores is determined by a standard scoring method, and the second biomarker score is determined by an intensity scoring method. In some embodiments, each of the second and third biomarker scores is determined by a standard scoring method, and the first biomarker score is determined by an intensity scoring method. In some embodiments, the first biomarker score is determined by a standard scoring method, and each of the second and third biomarker scores is determined by an intensity scoring method. In some embodiments, the second biomarker score is determined by a standard scoring method, and each of the first and third biomarker scores is determined by an intensity scoring method. In some embodiments, the third biomarker score is determined by a standard scoring method, and each of the first and second biomarker scores is determined by an intensity scoring method.

[0118] In some embodiments, the present disclosure includes the insight that standard scoring methods and intensity scoring methods can each be used with a given biomarker to detect a response prediction signature (e.g., for determining whether a patient is a responder to a Chk1 inhibitor therapy, a Wee1 inhibitor therapy, an ATR inhibitor therapy, or a combination thereof). For example, in some embodiments, a given biomarker may have two biomarker scores, one biomarker score determined by a standard scoring method and the other biomarker score determined by an intensity scoring method. In some embodiments, at least two biomarker scores are generated for a first biomarker. In some embodiments, at least two biomarker scores are generated for a second biomarker. In some embodiments, at least two biomarker scores are generated for a third biomarker.

[0119] One of ordinary skill in the art will understand that standard scoring is a variation of intensity scoring. For example, depending on the weights selected, a biomarker score based on a weighted signal intensity distribution may correspond to a biomarker score based on the percentage of positive cells. That is, standard scoring corresponds to assigning the same weight to all signal intensity bins greater than a background threshold. Similarly, one of ordinary skill in the art will understand that a background threshold is a form of intensity scoring. That is, a background threshold corresponds to assigning a weight of zero to all signal intensity range bins less than the background threshold.

[0120] In some embodiments, the present disclosure includes the insight that two or more biomarker scores may be combined (e.g., generally, by summing the biomarker scores) to generate a composite score for detecting a response prediction signature (e.g., for determining whether a patient is a responder to a Chk1 inhibitor therapy, a Wee1 inhibitor therapy, or an ATR inhibitor therapy, or a combination thereof). Using the composite score can enable the detection of response prediction signatures that may have a low value for one biomarker score but a high value for another biomarker score. For example, a tissue sample from a subject containing cells having a first biomarker score above a first and a third prediction threshold but a second biomarker score below a second prediction threshold can still be accurately predicted to be responsive by a composite score determined by combining the first, second, and third biomarker scores.

[0121] In some embodiments, one or more biomarker scores used to generate a composite score are transformed biomarker scores calculated by using a logarithmic or logistic function to perform a non-linear transformation of the original biomarker score. In some embodiments, one or more biomarker scores are weighted before being used to generate a composite score. In some embodiments, the first, second, and / or third biomarker scores may be weighted differently for the purpose of determining a composite score. In some embodiments, the first, second, and third biomarker scores are weighted equally. In some embodiments, the first, second, and third biomarker scores are each weighted differently. In some embodiments, when used to determine a composite score, each of the first, second, and third biomarker scores is weighted by a factor of one. In some embodiments, when used to determine a composite score, the first and second biomarker scores are weighted by a factor of one and the third biomarker score is weighted by a factor of two. In some embodiments, when used to determine a composite score, the first and third biomarker scores are weighted by a factor of one and the second biomarker score is weighted by a factor of two. In some embodiments, when used to determine a composite score, the second and third biomarker scores are weighted by a factor of one and the first biomarker score is weighted by a factor of two.

[0122] In some embodiments, the composite score is by combining biomarker scores for each of the first, second, and third biomarkers. In some embodiments, the composite score is generated by combining biomarker scores from only two of the first, second, and third biomarkers. In some embodiments, the composite score is generated by combining biomarker scores from the first and second biomarkers. In some embodiments, the composite score is generated by combining biomarker scores from the first and third biomarkers. In some embodiments, the composite score is generated by combining biomarker scores from the second and third biomarkers.

[0123] Prediction threshold Once a biomarker score has been determined for each biomarker to be evaluated, an analysis of each biomarker score relative to a corresponding threshold is performed to determine whether the patient is a predicted responder or non-responder to Chk1 inhibitor therapy, Wee1 inhibitor therapy, ATR inhibitor therapy, or combinations thereof. The identified thresholds are referred to herein as "prediction thresholds."

[0124] In some embodiments, if the sample includes first, second, and / or third biomarker scores each associated with a first, second, and / or third biomarker that exceed (or fall below in the case of a third biomarker such that a biomarker score below the predictive threshold is considered positive) the corresponding predictive threshold for each biomarker (by whether such a sample is required to have a specific biomarker score that exceeds a specific predictive threshold for subject selection or falls below for a specific third biomarker), the subject is predicted to be responsive to a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof. In some embodiments, if the sample includes one, two, three, or more biomarkers (e.g., the first, second, and third biomarkers) where each biomarker is associated with a biomarker score that is at or above the corresponding predictive threshold for that biomarker score, the tissue sample of the subject is determined to be responsive to a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof.

[0125] It is also understood that percentages relative to a predictive threshold used throughout this application may also be described as ratios (i.e., 3% is the same as 0.03, 5% is the same as 0.05, 50% is the same as 0.50, etc.).

[0126] The predictive thresholds described herein are, in some embodiments, derived from a retrospective analysis of a known population of subjects administered a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof. For example, in some embodiments, the predictive threshold is derived by analysis of samples from previous subjects who have received a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof and have been clinically determined to be either responsive or non-responsive. In some embodiments, the predictive threshold is derived from cancer cell lines or PDX models.

[0127] As described herein, the reference to the "first" prediction threshold corresponds to the prediction threshold of the first biomarker score derived from the analysis of the first biomarker described herein. Similarly, the "second" prediction threshold corresponds to the prediction threshold of the second biomarker score derived from the analysis of the second biomarker described herein, and the "third" prediction threshold corresponds to the prediction threshold of the third biomarker score derived from the analysis of the third biomarker described herein.

[0128] In some embodiments, a first prediction threshold is met when a tissue sample of interest includes a first biomarker associated with a first biomarker score that is greater than or equal to a first prediction threshold, where the first biomarker score is determined using a standard scoring method. In some embodiments, the first prediction threshold is met when the first biomarker score is about 3% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 4% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 5% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 6% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 7% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 8% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 9% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 10% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 20% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 30% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 40% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 50% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 60% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 70% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 80% or greater. In some embodiments, the first prediction threshold is met when the first biomarker score is about 90% or greater.

[0129] In some embodiments, the second prediction threshold is met when the tissue sample of interest includes a second biomarker associated with a second biomarker score that is greater than or equal to a second prediction threshold, where the second biomarker score is determined using a standard scoring method. In some embodiments, the second prediction threshold is met when the second biomarker score is about 3% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 4% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 5% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 6% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 7% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 8% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 9% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 10% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 20% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 30% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 40% or greater. In some embodiments, the second prediction threshold is approximately met when the second biomarker score is 50% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 60% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 70% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 80% or greater. In some embodiments, the second prediction threshold is met when the second biomarker score is about 90% or greater.

[0130] In some embodiments, the third prediction threshold is met when the tissue sample of interest includes a third biomarker associated with a third biomarker score that is greater than or equal to a third prediction threshold, where the third biomarker score is determined using a standard scoring method. In some embodiments, the third prediction threshold is met when the third biomarker score is about 3% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 4% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 5% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 6% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 7% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 8% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 9% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 10% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 20% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 30% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 40% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 50% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 60% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 70% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 80% or greater. In some embodiments, the third prediction threshold is met when the third biomarker score is about 90% or greater.

[0131] In some embodiments, a third prediction threshold is met if a tissue sample of interest includes a third biomarker associated with a third biomarker score that is below a third prediction threshold, where the third biomarker score is determined using a standard scoring method. In some embodiments, the third prediction threshold is met if the third biomarker score is about 50% or less. In some embodiments, the third prediction threshold is met if the third biomarker score is about 40% or less. In some embodiments, the third prediction threshold is met if the third biomarker score is about 30% or less. In some embodiments, the third prediction threshold is met if the third biomarker score is about 20% or less. In some embodiments, the third prediction threshold is met if the third biomarker score is about 10% or less. In some embodiments, the third prediction threshold is about 5% or less.

[0132] In some embodiments, the first, second, and third prediction thresholds are met if the first, second, and third biomarker scores are each 3% or more. In some embodiments, the first, second, or third prediction threshold is met if the first, second, or third biomarker score is from about 3% to about 25%. In some embodiments, a subject is responsive if the first prediction threshold is 5% or more, the second threshold is 5% or more, and the third prediction threshold is 30% or less.

[0133] In some embodiments, a subject is determined to be responsive if a sample of the subject's cells includes the first, second, and third biomarkers, where the first biomarker is detected by the first biomarker score at an amount of 5% or more of a first prediction threshold, the second biomarker is detected by the second biomarker score at an amount of 30% or more of a second prediction threshold, and the third biomarker is detected by the third biomarker score at an amount of 5% or more of a third prediction threshold.

[0134] In some embodiments, the subject is determined to be responsive when a sample of the subject's cells includes first, second, and third biomarkers, where the first biomarker is detected by a first biomarker score at a first prediction threshold of an amount that is 5% or greater, the second biomarker is detected by a second biomarker score at a second prediction threshold of an amount that is 5% or greater, and the third biomarker is detected by a third biomarker score at a third prediction threshold of an amount that is 5% or greater. In some aspects of these embodiments, the first biomarker is detected by a first biomarker score at a first prediction threshold of an amount between 5% and 10% (e.g., 5%, 6%, 7%, 8%, 9%, or 10%), the second biomarker is detected by a second biomarker score at a second prediction threshold of an amount between 5% and 10% (e.g., 5%, 6%, 7%, 8%, 9%, or 10%), and the third biomarker is detected by a third biomarker score at a third prediction threshold of an amount between 5% and 10% (e.g., 5%, 6%, 7%, 8%, 9%, or 10%). In some more specific aspects of these embodiments, the first biomarker is detected by a first biomarker score at a first prediction threshold of 5%, the second biomarker is detected by a second biomarker score at a second prediction threshold of 5%, and the third biomarker is detected by a third biomarker score at a third prediction threshold of 5%.

[0135] As noted above, the present disclosure recognizes that there are multiple ways to generate a biomarker score (e.g., by using a standard scoring method or by using an intensity scoring method), and thus two or more biomarker scores may be used for a given biomarker. In some embodiments, for a given biomarker having two or more biomarker scores, in order to determine that a subject is a predicted responder to a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof, each of the + biomarker scores for that biomarker must meet its corresponding prediction threshold. In some embodiments, for a given biomarker having two or more biomarker scores, in order to determine that a subject is a predicted responder to a Chk1 inhibition therapy, a Wee1 inhibition therapy, an ATR inhibition therapy, or a combination thereof, at least one of the biomarker scores for that biomarker must not meet its corresponding prediction threshold. For example, in some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition, the method comprising administering a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof to a subject determined to be associated with (a) a first biomarker associated with a biomarker score determined using a standard scoring method that is (i) above the corresponding prediction threshold, and / or (ii) a biomarker score determined using an intensity scoring method that is above the corresponding prediction threshold; (b) a second biomarker associated with a biomarker score determined using a standard scoring method that is (i) above the corresponding prediction threshold, and / or (ii) a biomarker score determined using an intensity scoring method that is above the corresponding prediction threshold; and (c) a third biomarker associated with a biomarker score determined using a standard scoring method that is (i) above the corresponding prediction threshold, and / or (ii) a biomarker score determined using an intensity scoring method that is above the corresponding prediction threshold.

[0136] As described above, the present disclosure recognizes that two or more biomarker scores can be combined to generate a composite score for detecting a response prediction signature. In some embodiments, the prediction threshold is a relative threshold at which the composite score is analyzed to determine whether a subject is a predicted responder to a Chk1 inhibitor therapy, a Wee1 inhibitor therapy, an ATR inhibitor therapy, or a combination thereof. The identified threshold is referred to herein as the "composite threshold".

[0137] For example, if the composite score is determined using a standard scoring method, for each of the three biomarkers after multiplying the second biomarker score by 2, the composite score may be determined for a patient sample having 1 / 4 or 25% of the cells for which each of the first, second, and third biomarkers is positive by taking the sum of the biomarker scores. In such an example, the sample would have a composite score of 0.25+(2×0.25)+0.25 = 1.0. If the composite threshold is 0.9 or greater, a patient having a composite score of 1.0 would be predicted to be a responder to a Chk1 inhibitor therapy, a Wee1 inhibitor therapy, an ATR inhibitor therapy, or a combination thereof.

[0138] As described herein, the composite score corresponds to a composite threshold of responsiveness. In some embodiments, the composite threshold is met when the tissue sample of interest contains two or more of the first, second, and third biomarkers associated with a composite score that is greater than or equal to the composite threshold. In some embodiments, the composite threshold is met when the composite score is 0.9 or greater. In some embodiments, the composite threshold is met when the composite score is 1.0 or greater. In some embodiments, the composite threshold is met when the composite score is 1.2 or greater. In some embodiments, the composite threshold is met when the composite score is 1.5 or greater. In some embodiments, the composite threshold is met when the composite score is 1.7 or greater. In some embodiments, the composite threshold is met when the composite score is 2.0 or greater. In some embodiments, the composite threshold is met when the composite score is 2.2 or greater. In some embodiments, the composite threshold is met when the composite score is 2.5 or greater. In some embodiments, the composite threshold is met when the composite score is 2.7 or greater. In some embodiments, the composite threshold is met when the composite score is 3.0 or greater.

[0139] In some embodiments, to predict a response to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, it is also understood that detection of two out of three biomarkers, each at a biomarker score above a prediction threshold or combined as a composite score above a composite threshold (e.g., in total), is sufficient. In some embodiments, the first biomarker and the second biomarker are detected as having first and second biomarker scores above the first and second prediction thresholds. In some embodiments, the first biomarker and the third biomarker are detected as having first and third biomarker scores above the first and third prediction thresholds. In some embodiments, the second biomarker and the third biomarker are detected as having second and third biomarker scores above the second and third prediction thresholds. In some embodiments, the prediction threshold for a given biomarker is higher for that biomarker when only two rather than three biomarkers are used. By way of example, the first prediction threshold for the first biomarker may be 5%, 10%, 15%, 20%, etc. higher than the first prediction threshold when all three biomarkers are detected, when used in combination with only one other biomarker.

[0140] In some embodiments, it is also understood that in order to predict the response to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, detection of one of the three biomarkers with a biomarker score above a prediction threshold is sufficient. In some embodiments, a first biomarker having a first biomarker score above a first prediction threshold is detected. In some embodiments, a second biomarker having a second biomarker score above a second prediction threshold is detected. In some embodiments, a third biomarker having a third biomarker score above a third prediction threshold is detected. In some embodiments, the prediction threshold for a given biomarker is higher with respect to that biomarker when only one biomarker, rather than two or three biomarkers, is used. By way of example, the first prediction threshold for the first biomarker may be 5%, 10%, 15%, 20%, etc. higher than the first prediction threshold when all two or three biomarkers are detected and used alone. In some embodiments, in order to predict the response to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof using a single biomarker, detection of the biomarker within a specific target region (e.g., within tumor cells or within the tumor cell nucleus) is required.

[0141] In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with a Wee1 inhibitor, the third biomarker is not total cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with a Wee1 inhibitor, the third biomarker is not Ser100 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with a Wee1 inhibitor, the third biomarker is not Ser387 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with a Wee1 inhibitor, the third biomarker is not Thr395 phosphorylated cyclin E1.

[0142] In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with adavosertib, the third biomarker is not total cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with adavosertib, the third biomarker is not Ser100 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with adavosertib, the third biomarker is not Ser387 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with adavosertib, the third biomarker is not Thr395 phosphorylated cyclin E1.

[0143] In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with azenocarb, the third biomarker is not total cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with azenocarb, the third biomarker is not Ser100 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with azenocarb, the third biomarker is not Ser387 phosphorylated cyclin E1. In some embodiments, when a third biomarker is used alone for detecting a response prediction signature for treatment with azenocarb, the third biomarker is not Thr395 phosphorylated cyclin E1.

[0144] DNA Damage Response and Cell Cycle Checkpoints To protect themselves from DNA damage, multicellular organisms have evolved a DNA damage response (DDR) program consisting of DNA replication stress, cell cycle progression, and multiple checkpoints associated with DNA damage. Here, Chk1 and Chk2 are important mediators as shown in FIGS. 1A and 1B. See Smith et al., Advances in Cancer Research, vol. 108, p73-112 (2010); Hong et al., Clin. Cancer Res. 24(14):3263-3272 (2018). As shown in FIG. 2, Chk1 and Chk2 are activated by the upstream kinases Ataxia-Telangiectasia and RAD3-related protein (ATR) and Ataxia-Telangiectasia Mutated (ATM), respectively. Activation of the ATR-Chk1 or ATM-Chk2 signaling pathway results in phosphorylation and inhibition of the CDC25 phosphatase, leading to cell cycle arrest.

[0145] At the G2 / M checkpoint, as shown in Figure 3, Chk1 activates the Wee1 kinase and inhibits the CDC25 phosphatase. Matheson et al. Trends in Pharmacological Sciences, vol 37, p872-881 (2016). Chk1 phosphorylates Wee1 at serine 642, CDC25B at serine 230, and CDC25C at serine 216. Perry et al., Cell Division, vol.2, p1-12 (2007), Schmitt et al., Journal of Cell Science, vol.119, p4269-4275 (2006). The activation of Wee1 and the inhibition of the CDC25 proteins result in the inhibition of the CDK1 and CDK2 kinases, which are the main drivers of DNA replication and mitosis. By inhibiting CDK, the cell cycle is arrested, allowing the cell to gain time for DNA repair. The phosphorylation of CDC25A involving Chk1 leads to the degradation of CDC25A, whereas the phosphorylation of CDC25B and CDC25C leads to the cytosolic sequestration of these proteins. CDC25A is an important regulator of CDK2 and DNA replication. King et al., Mol.Cancer Ther., 14(9):2004-2013. The loss of CDC25A activity involving CHk1 keeps CDK2 in an inactive phosphorylated state, preventing the initiation of DNA replication origins, and the cytosolic sequestration of CDC25B and CDC25C inhibits the activation of CDK1 and prevents early mitosis. Ibid.

[0146] One skilled in the art will understand that a response prediction signature that can predict sensitivity to a Chk1 inhibitor, such as prexasertib, will also predict sensitivity to a Wee1 inhibitor if Wee1 phosphorylated by Chk1 at serine 642 is a direct mediator of Chk1 signaling.

[0147] One of ordinary skill in the art will understand that a response prediction signature that can predict sensitivity to a Chk1 inhibitor, such as prexasertib, will also predict sensitivity to an ATR inhibitor when ATR is immediately upstream of Chk1 and activates Chk1 by phosphorylation.

[0148] Consistent with the close functional relationship and direct interaction between these proteins, as demonstrated by certain embodiments disclosed herein (e.g., Example 8), there is a very strong correlation between WEE1 or ATR gene dependency and CHEK1 dependency. This correlation was shown by RNAi screening across a large panel of cancer cell lines. Similarly, screening of cell lines treated with inhibitors targeting any of ATR, Wee1, or Chk1 shows highly correlated profiles. Furthermore, the present disclosure demonstrates that various cancer lines show a similar response to two different Wee1 inhibitors as to prexasertib. Based on these results, without being bound by theory, the hypothesis is put forward that the various biomarker-based response prediction signatures described herein with respect to Chk1 inhibitors (and in particular prexasertib) are also useful for identifying subjects who will respond to Wee1 inhibitors or ATR inhibitors.

[0149] Checkpoint kinase 2 (Chk2) is a serine / threonine kinase that, like checkpoint kinase 1, plays an important role in the DNA damage repair pathway. Activated Chk2 phosphorylates its substrates, including BRCA1, E2F1, p53, CDC25A, and CDC25C, and induces an appropriate cellular response. Tian et al., Drug Design, Development, and Therapy, 14:2613 - 2622 (2020). Chk2 also affects tumor progression, for example, by destabilizing or disabling DNA repair to kill cells. Ibid.

[0150] Phosphorylated state Activation of Chk1 and Chk2 is accompanied by phosphorylation at specific sites. Chk1 and Chk2 are serine / threonine kinases that are phosphorylated at specific positions in response to replication stress or DNA damage. Figure 2 provides a diagram showing the regulation of Chk1 by phosphorylation. When activated, Chk1 and Chk2 phosphorylate various substrate proteins, causing cell cycle arrest, DNA repair, and / or cell death. Patil et al., Cell Mol. Life Sci., 70(21):4009-4021(2013).

[0151] Oncogenic virus It is also recognized that certain oncogenic viruses may affect Chk1 / Chk2 regulation. In some embodiments, the disease, disorder, or condition is associated with an oncogenic virus. In some embodiments, the disease, disorder or condition is associated with human papillomavirus, hepatitis C virus, hepatitis B virus, and Epstein-Barr virus.

[0152] In some embodiments, the oncogenic virus is a human papillomavirus. The human papillomavirus (HPV) is a risk factor for multiple human cancers, and 90% of cervical cancer, vulvar cancer, vaginal cancer, anal cancer, penile cancer, and oral cancer are somewhat associated with HPV. Certain HPV strains, such as the high-risk strains HPV16, 18, 31, and 35, are oncogenic, and these have been found to cause other genital cancers, including cervical cancer and anal cancer, vulvar cancer, vaginal cancer, penile cancer, and oral cancer. Without being bound by theory, it is understood that HPV replication after infection results in an "onion skin" structure, causing torsional stress and promoting the formation of single-stranded DNA and double-strand breaks that will be recognized by the cell as DNA damage. During the establishment phase of the cell cycle, HPV replication activates local DDR by the abnormal DNA structure of the viral genome. In some embodiments, HPV infection activates STAT-5 (associated with cell proliferation, apoptosis, and differentiation), causing phosphorylation of STAT-5, which controls genomic amplification by activation of ATM DDR. Inhibition of STAT-5 suppresses phosphorylation of ATM, Chk2, BRCA1, and RAD51, thereby preventing amplification of the HPV genome. The HPV strain E7 induces activation of ATM, which activates downstream effectors BRCA1, Chk2, and p53, leading to cell cycle arrest and HPV amplification. See generally Hong et al., Future Microbiol., 8(12):1547-1557 (2013); Hong et al., mBio., 6(6):e02006-15 (Nov-Dec 2015).

[0153] Phosphorylation of STAT-5 by HPV also increases TopBP1 levels, which activates the ATR signaling pathway. HPV can also directly phosphorylate TopBP1 to control E2F1 transcriptional activity. DNA synthesis and genomic replication of HPV are promoted by ATR / Chk1 activation, and the DDR of ATR / Chk1 controls cell cycle arrest by E2F1 in HPV-positive cells.

[0154] In some embodiments, HPV-positive cells exhibit an increase in the overall and phosphorylated levels of DNA damage repair proteins, including, for example, ATR, Chk1, TOPBP1, FANCD2, RAD51, NBS1, BRCA1, 53BP1, RNF168, and Tip60. In some embodiments, HPV-positive cells contain phosphorylated forms of proteins, including increased levels of pATM, pChk2, pNBS1, pBRCA1, γH2AX, pATR, pChk1, pTOPBP1, pSMC1, and pMRE11.

[0155] In some embodiments, the oncogenic virus is the hepatitis B or C virus. Hepatitis B and C viruses (HBV and HCV, respectively) are associated with, for example, liver cancer, and 65% of liver cancer cases are associated with HBV / HCV, with 50% due to HCV alone. HBV / HCV requires DDR for efficient replication of infected cells and increases the levels of ATR and phosphorylated Chk1. The cytoplasmic HBX (HBx) protein, an important factor in HBV cccDNA (covalently closed circular DNA) transcription, induces endoplasmic reticulum stress and DNA damage by activating the Chk2 signaling cascade, increasing HBV transcription and replication. Inhibitors of ATM, ATR, or Chk1 suppress HBV replication and HBV-related pathogenesis. Furthermore, HCV results in the activation of ATM. ATM transcript levels are significantly higher in patients chronically infected with HCV. Patra et al, Hepatology, 2020 March;71(3):780-793.

[0156] In some embodiments, the oncogenic virus is Epstein-Barr virus. Epstein-Barr virus (EBV) dysregulates DDR transducers. EBV-infected nasopharyngeal cells exhibit consistent ATM activation. Inhibition of ATM inhibits viral DNA replication.

[0157] Generally, viral oncoproteins activate cellular oncogenes to enter the cell cycle, induce replication stress, and cause DNA single-strand breaks. Single-strand and double-strand breaks in DNA generated during single-strand DNA repair are recognized by ATR and ATM, respectively, and control downstream signaling, activation of Chk2, and p53.

[0158] Diseases, disorders, and conditions The methods and systems described herein are useful for the treatment of the diseases, disorders, and conditions described herein. In some embodiments, the disease, disorder, or condition is related to Chk1. In some embodiments, the Chk1-related disease, disorder, or condition is mediated or regulated by a member of the ATR / Chk1 signaling pathway (e.g., Chk1, Chk2, Wee1, ATR, or combinations thereof) (e.g., a protein or its phosphorylated state). In some embodiments, the disease, disorder, or condition is mediated or regulated by Chk1, Chk2, Wee1, ATR, or combinations thereof. In some embodiments, the disease, disorder, or condition is mediated or regulated by Chk1. In some embodiments, the disease, disorder, or condition is mediated or regulated by Chk2. In some embodiments, the disease, disorder, or condition is mediated or regulated by Wee1. In some embodiments, the disease, disorder, or condition is mediated or regulated by ATR.

[0159] In some embodiments, the disease, disorder, or condition is a Chk1-mediated disease. In some embodiments, the disease, disorder, or condition is cancer. In some embodiments, the cancer is ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, colorectal cancer, pancreatic cancer, or sarcoma. In some embodiments, the cancer is anal cancer. In some embodiments, the anal cancer is squamous cell carcinoma of the anus. In some embodiments, the cancer is cervical cancer. In some embodiments, the cancer is head and neck cancer. In some embodiments, the head and neck cancer is squamous cell carcinoma of the head and neck. In some embodiments, the cancer is lung cancer. In some embodiments, the lung cancer is small cell lung cancer. In some embodiments, the lung cancer is non-small cell lung cancer (e.g., adenocarcinoma of the lung or squamous cell carcinoma of the lung). In some embodiments, the cancer is bladder cancer. In some embodiments, the cancer is liver cancer. In some embodiments, the cancer is breast cancer. In some embodiments, the breast cancer is luminal A breast cancer. In some embodiments, the cancer is endometrial cancer. In some embodiments, the cancer is serous endometrial cancer. In some embodiments, the cancer is uterine carcinosarcoma. In some embodiments, the cancer is ovarian cancer. In some embodiments, the ovarian cancer is high-grade serous ovarian cancer. In some embodiments, the cancer is colorectal cancer. In certain embodiments, the cancer is pancreatic cancer. In some embodiments, the cancer is medulloblastoma. In some embodiments, the cancer is sarcoma. In some embodiments, the sarcoma is desmoplastic small round cell tumor. In some embodiments, the sarcoma is rhabdomyosarcoma. In some embodiments, the disease, disorder, or condition is a solid tumor.

[0160] In some embodiments, the disease, disorder, or condition is a Wee1-mediated disease. In some embodiments, the disease, disorder, or condition is cancer. In some embodiments, the cancer is ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, colorectal cancer, pancreatic cancer, or sarcoma. In some embodiments, the cancer is anal cancer. In some embodiments, the anal cancer is squamous cell carcinoma of the anus. In some embodiments, the cancer is cervical cancer. In some embodiments, the cancer is head and neck cancer. In some embodiments, the head and neck cancer is squamous cell carcinoma of the head and neck. In some embodiments, the cancer is lung cancer. In some embodiments, the lung cancer is small cell lung cancer. In some embodiments, the lung cancer is non-small cell lung cancer (e.g., adenocarcinoma of the lung or squamous cell carcinoma of the lung). In some embodiments, the cancer is bladder cancer. In some embodiments, the cancer is liver cancer. In some embodiments, the cancer is breast cancer. In some embodiments, the breast cancer is luminal A breast cancer. In some embodiments, the cancer is endometrial cancer. In some embodiments, the cancer is serous endometrial cancer. In some embodiments, the cancer is uterine carcinosarcoma. In some embodiments, the cancer is ovarian cancer. In some embodiments, the ovarian cancer is high-grade serous ovarian cancer. In some embodiments, the cancer is colorectal cancer. In certain embodiments, the cancer is pancreatic cancer. In some embodiments, the cancer is medulloblastoma. In some embodiments, the cancer is sarcoma. In some embodiments, the sarcoma is fibromatosis round cell tumor. In some embodiments, the sarcoma is rhabdomyosarcoma. In some embodiments, the disease, disorder, or condition is a solid tumor.

[0161] In some embodiments, the disease, disorder, or condition is an ATR-mediated associated disease. In some embodiments, the disease, disorder, or condition is cancer. In some embodiments, the cancer is ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, colorectal cancer, pancreatic cancer, or sarcoma. In some embodiments, the cancer is anal cancer. In some embodiments, the anal cancer is anal squamous cell carcinoma. In some embodiments, the cancer is cervical cancer. In some embodiments, the cancer is head and neck cancer. In some embodiments, the head and neck cancer is head and neck squamous cell carcinoma. In some embodiments, the cancer is lung cancer. In some embodiments, the lung cancer is small cell lung cancer. In some embodiments, the lung cancer is non-small cell lung cancer (e.g., lung adenocarcinoma or lung squamous cell carcinoma). In some embodiments, the cancer is bladder cancer. In some embodiments, the cancer is liver cancer. In some embodiments, the cancer is breast cancer. In some embodiments, the breast cancer is luminal A breast cancer. In some embodiments, the cancer is endometrial cancer. In some embodiments, the cancer is serous endometrioid carcinoma. In some embodiments, the cancer is uterine carcinosarcoma. In some embodiments, the cancer is ovarian cancer. In some embodiments, the ovarian cancer is high-grade serous ovarian carcinoma. In some embodiments, the cancer is colorectal cancer. In certain embodiments, the cancer is pancreatic cancer. In some embodiments, the cancer is medulloblastoma. In some embodiments, the cancer is a sarcoma. In some embodiments, the sarcoma is desmoplastic small round cell tumor. In some embodiments, the sarcoma is rhabdomyosarcoma. In some embodiments, the disease, disorder, or condition is a solid tumor.

[0162] In some embodiments, the cancer is a recurrent or metastatic cancer. In some aspects of these embodiments, the combination of biomarkers is any one of the combinations set forth in Table A above.

[0163] In some embodiments, the disease, disorder, or condition is associated with an oncogenic virus. In some aspects of these embodiments, the combination of biomarkers is any one of the combinations listed in Table A above.

[0164] Therapeutic agent The therapeutic agent is an inhibitor of the expression or activity (e.g., kinase activity) of a protein in the ATR / Chk1 signaling pathway. In some embodiments, the therapeutic agent is a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. In some embodiments, the therapeutic agent is a Chk1 inhibitor. In some embodiments, the therapeutic agent is a Chk1 inhibitor and the combination of biomarkers is any one of the combinations listed in Table A above. In some embodiments, the therapeutic agent is a Wee1 inhibitor. In some embodiments, the therapeutic agent is a Wee1 inhibitor and the combination of biomarkers is any one of the combinations listed in Table A above. In some embodiments, the therapeutic agent is an ATR inhibitor. In some embodiments, the therapeutic agent is an ATR inhibitor and the combination of biomarkers is any one of the combinations listed in Table A above. In some embodiments, the therapeutic agent is a Chk1 inhibitor and a Wee1 inhibitor. In some embodiments, the therapeutic agent is a Chk1 inhibitor and an ATR inhibitor. In some embodiments, the therapeutic agent is a Wee1 inhibitor and an ATR inhibitor. In some embodiments, the therapeutic agent is a Chk1 inhibitor, a Wee1 inhibitor, and an ATR inhibitor.

[0165] In some embodiments, the subject is administered two or more therapeutic agents. In some embodiments, the subject is administered a Chk1 inhibitor and one or both of a Wee1 inhibitor and an ATR inhibitor. In some embodiments, the subject is administered a Chk1 inhibitor and a Wee1 inhibitor. In some embodiments, the subject is administered a Chk1 inhibitor and an ATR inhibitor. In some embodiments, the subject is administered a Wee1 inhibitor and an ATR inhibitor. In some embodiments, the subject is administered a Chk1 inhibitor, a Wee1 inhibitor, and an ATR inhibitor.

[0166] The terms "checkpoint kinase 1 inhibitor" and "Chk1 inhibitor" are used interchangeably herein and refer to an agent (e.g., small molecule, biologic, etc.) having inhibitory activity against checkpoint kinase 1. Some Chk1 inhibitors also inhibit other kinases to some extent depending on selectivity. Some Chk1 inhibitors also inhibit Chk2 to some extent and may be referred to herein as either a Chk1 inhibitor or a Chk1 / Chk2 inhibitor. Notwithstanding the foregoing, the terms "checkpoint kinase 1 inhibitor" and "Chk1 inhibitor" do not include compounds that primarily inhibit Chk2 but do not significantly inhibit Chk1.

[0167] The terms "Wee1 inhibitor" and "ATR inhibitor" each refer to an agent (e.g., small molecule, biologic, etc.) having inhibitory activity against the Wee1 kinase or the ATR kinase, respectively.

[0168] Inhibitors of Chk1 have emerged as promising therapies for treating cancer by preventing activation of the DNA damage checkpoint and, in certain therapies, causing DNA breaks that lead to cell death. In particular, prexasertib, also known as LY2606368, a Chk1 / Chk2 inhibitor, has been developed as a therapy for treating, among other things, advanced squamous cell carcinoma, head and neck SCC, anal SCC, and squamous cell non-small cell lung cancer. Hong et al., Clin. Cancer Res. 24(14):3263 - 3272 (2018).

[0169] Many checkpoint kinase 1 inhibitors are currently under investigation. In some embodiments, the checkpoint kinase 1 inhibitor is selected from prexasertib, SRA737, PHI-101, LY2880070, V158411, CASC-578, IMP10, SOL-578, rubexasertib, AZD7762, MK-8776, PF-00477736, GDC-0575, XL-844, CBP501, BBI-355, PEP07, and VER-250840. In some embodiments, the checkpoint kinase 1 inhibitor is selected from prexasertib, SRA737, PHI-101, LY2880070, V158411, CASC-578, IMP10, and SOL-578. In some embodiments, the checkpoint kinase 1 inhibitor is selected from rubexasertib, AZD7762, MK-8776, PF-00477736, GDC-0575, or XL-844.

[0170] In some embodiments, the Chk1 inhibitor is prexasertib, or a pharmaceutically acceptable salt thereof. In some embodiments, the Chk1 inhibitor is prexasertib, or a pharmaceutically acceptable salt thereof, and the biomarker combination is any one of the combinations described in Table A. Prexasertib (also known as LY2606368 and ACR-368) is a checkpoint kinase 1 and 2 inhibitor that has been shown to be active as a treatment for various cancers, including sarcoma, in some subjects. Prexasertib as monotherapy results in a "potent decrease in clonogenic survival" by interfering with cell cycle progression, inducing apoptosis, and inducing double-strand DNA breaks. Heidler et al., Int. J. Cancer, 147(4):1059-1070 (2020). Clinical trials of prexasertib are still ongoing, including studies examining the efficacy of prexasertib in the treatment of acute myeloid leukemia, myelodysplastic syndromes, rhabdomyosarcoma, medulloblastoma, and high-grade serous ovarian cancer. Prexasertib is a compound of the following structure, [Chemistry] It is otherwise known as 5-(5-(2-(3-aminopropoxy)-6-methoxyphenyl)-1H-pyrazol-3-ylamino)pyrazine-2-carbonitrile and is described in WO2010 / 077758 and WO2017 / 100071, the entireties of which are incorporated herein by reference.

[0171] Hong et al. attempted to identify biomarkers for predicting response to prexasertib, but to date, a reliable and consistent signature has not been identified. The response rate to prexasertib in clinical trials typically ranges between 5% and 20% across cancers including head and neck cancer, anal cancer, and ovarian cancer, and prexasertib has shown clinical single-agent activity. Only one study, a single-institution phase 2 trial of ovarian cancer conducted at the NCI led by Dr. Lee, showed a somewhat higher response rate of 29% to single-agent prexasertib therapy. See Lee, Lancet Oncol. 19(2):207-215 (2018). The response duration across responsive cancers is surprisingly between about 6 months and over 12 months. See Hong et al., Clin. Cancer Res. 24(14):3263-3272 (2018). The side effects of prexasertib are reversible but common and include hematologic, mechanism-based myelosuppression including neutropenia, thrombocytopenia, and anemia. Lee, Lancet Oncol. 19(2):207-215 (2018); Hong et al., Clin Cancer Res 24(14):3263-3272 (2018).

[0172] In some embodiments, the checkpoint kinase 1 inhibitor is a pharmaceutically acceptable salt of prexasertib. In some embodiments, the therapy described herein is prexasertib mesylate hydrate. [Chemistry]

[0173] In some embodiments, the therapy described herein is prexasertib (S)-lactate monohydrate.

Chem.

[0174] In some embodiments, the checkpoint kinase 1 inhibitor is SRA737 (also known as CCT245737), which is a small molecule of the following structure.

Chem.

[0175] In some embodiments, the checkpoint kinase 1 inhibitor is PHI-101, which is a small molecule of the following structure.

Chem.

[0176] In some embodiments, the checkpoint kinase 1 inhibitor is LY2880070 (also known as ESO-01), which is a small molecule of the following structure.

Chem.

[0177] In some embodiments, the checkpoint kinase 1 inhibitor is V158411, which is a small molecule of the following structure. [Chem.] In some embodiments, the Chk1 inhibitor is V158411, and the combination of biomarkers is any one of the combinations listed in Table A.

[0178] In some embodiments, the checkpoint kinase 1 inhibitor is CASC-578. In some embodiments, the Chk1 inhibitor is CASC-578, and the combination of biomarkers is any one of the combinations listed in Table A.

[0179] In some embodiments, the checkpoint kinase 1 inhibitor is IMP10. In some embodiments, the Chk1 inhibitor is IMP10, and the combination of biomarkers is any one of the combinations listed in Table A.

[0180] In some embodiments, the checkpoint kinase 1 inhibitor is SOL-578. In some embodiments, the Chk1 inhibitor is SOL-578, and the combination of biomarkers is any one of the combinations listed in Table A.

[0181] In some embodiments, the Wee1 inhibitor is selected from adavosertib (AZD1775, MK1775), asenocertib (Zn-C3), Debio0123, STC8123, ATRN-1051, NUV-569, IMP7068, BBI-355, PEP07, SPH-6162, and ZSY-4835. In some embodiments, the Wee1 inhibitor is selected from adavosertib (AZD1775, MK1775), asenocertib (Zn-C3), Debio0123, STC8123, ATRN-1051, NUV-569, and IMP7068. In some embodiments, the Wee1 inhibitor is adavosertib (AZD1775, MK1775). In some embodiments, the Wee1 inhibitor is adavosertib, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is asenocertib (Zn-C3). In some embodiments, the Wee1 inhibitor is asenocertib, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is Debio0123. In some embodiments, the Wee1 inhibitor is Debio0123, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is STC8123. In some embodiments, the Wee1 inhibitor is STC8123, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is ATRN-1051. In some embodiments, the Wee1 inhibitor is ATRN-1051, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is NUV-569. In some embodiments, the Wee1 inhibitor is NUV-569, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is IMP7068.In some embodiments, the Wee1 inhibitor is IMP7068, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is BBI-355. In some embodiments, the Wee1 inhibitor is BBI-355, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is PEP07. In some embodiments, the Wee1 inhibitor is PEP07, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is SPH6162. In some embodiments, the Wee1 inhibitor is SPH6162, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the Wee1 inhibitor is ZSY-4835. In some embodiments, the Wee1 inhibitor is ZSY-4835, and the biomarker combination is any one of the combinations listed in Table A.

[0182] In some embodiments, the therapeutic agent is a Wee1 inhibitor. In some embodiments, the Wee1 inhibitor is not adavosertib (AZD1775, MK1775).

[0183] In some embodiments, the ATR inhibitor is selected from veliparib (M6620, VX-970), galparib (M4344, VX-803), elimusertib (BAY1895344), ceralasertib (AZD6738), M1774, ATRN-119, camonsertib (RP-3500), ART0380, and ATG018. In some embodiments, the ATR inhibitor is veliparib (M6620, VX-970). In some embodiments, the ATR inhibitor is veliparib, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is galparib (M4344, VX-803). In some embodiments, the ATR inhibitor is galparib, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is elimusertib (BAY1895344). In some embodiments, the ATR inhibitor is elimusertib, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is ceralasertib (AZD6738). In some embodiments, the ATR inhibitor is ceralasertib, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is M1774. In some embodiments, the ATR inhibitor is M1774, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is ATRN-119. In some embodiments, the ATR inhibitor is ATRN-119, and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is camonsertib (RP-3500). In some embodiments, the ATR inhibitor is camonsertib (RP-3500), and the biomarker combination is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is ART0380.In some embodiments, the ATR inhibitor is ART0380, and the combination of biomarkers is any one of the combinations listed in Table A. In some embodiments, the ATR inhibitor is ATG018. In some embodiments, the ATR inhibitor is ATG018, and the combination of biomarkers is any one of the combinations listed in Table A.

[0184] DNA synthesis inhibitor The present disclosure further encompasses the insight that DNA synthesis inhibitors induce sensitivity to Chk1 inhibitors, Wee1 inhibitors, ATR inhibitors, or combinations thereof. In some embodiments, the present disclosure provides a method of treating a disease, disorder, or condition described herein (e.g., a Chk1-mediated disease, a Wee1-mediated disease, and / or an ATR-mediated disease), the method comprising administering a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof together with a DNA synthesis inhibitor. The DNA synthesis inhibitor is, in some embodiments, administered concomitantly with a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof.

[0185] Chk1 regulates the S and G2 / M checkpoints and is important for maintaining potential replication origins during S phase to prevent unscheduled replication firing. Normal cells have multiple functional cell cycle checkpoints, but many cancers have defects in specific checkpoints and thus rely heavily on other checkpoints for survival. For example, cancer cells with non-functional p53 have defects in the G1 / S cell cycle checkpoint and thus rely heavily on functional S and G2 / M checkpoints and the corresponding Chk1 activity for proper DNA replication and repair. See, for example, Bartek et al., Cancer Cell, 4:421-429 (2003). Without wishing to be bound by any particular theory, the present disclosure includes the insight that inhibition of DNA synthesis increases DNA replication stress and dependence on Chk1 and stabilizes stalled replication forks, such that DNA synthesis inhibitors can induce sensitivity to therapeutic agents such as Chk1 inhibitors, Wee1 inhibitors, ATR inhibitors, or combinations thereof.

[0186] In some embodiments, a DNA synthesis inhibitor and a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof are administered to a subject in whom it has been determined that the tissue sample does not exhibit a response prediction signature. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and a DNA synthesis inhibitor are administered simultaneously. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and a DNA synthesis inhibitor are administered continuously and simultaneously. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof is administered after administration of the DNA synthesis inhibitor. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and a DNA synthesis inhibitor are administered as a single composition. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and a DNA synthesis inhibitor are administered separately, for example, as separate compositions, by separate routes of administration (e.g., one agent orally and another agent intravenously) and / or at different times. In some embodiments, a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof, and a DNA synthesis inhibitor are administered together as a combination composition, by the same route of administration, and / or simultaneously.

[0187] In some embodiments, administration of a DNA synthesis inhibitor to a subject determined not to exhibit a response prediction signature for a therapeutic agent (e.g., a Chk1 inhibitor, a Wee1 inhibitor, and / or an ATR inhibitor) enhances the subject's sensitivity to the therapeutic agent such that the subject comes to exhibit the response prediction signature. In other words, in some embodiments, administration of the DNA synthesis inhibitor to the subject converts the subject from a non-responder to the therapeutic agent to a responder to the therapeutic agent. In some embodiments, a subject whose tissue sample exhibits a response prediction signature for the therapeutic agent after administration of the DNA synthesis inhibitor is administered the therapeutic agent. In other embodiments, a subject whose tissue sample does not exhibit the response prediction signature is administered the DNA synthesis inhibitor prior to (e.g., 5, 10, 15, 20, 25, 30, 45, 60, or 120 minutes prior, or 3, 4, 5, 6, 8, 12, or 24 hours prior to) or simultaneously with the therapeutic agent.

[0188] In some embodiments, the DNA synthesis inhibitor is a nucleoside analog. In some embodiments, the DNA synthesis inhibitor is a purine nucleoside analog (e.g., cladribine, clofarabine, fludarabine, nelarabine, pentostatin, tiazofurin, 6-mercaptopurine, and 6-thioguanine). In some embodiments, the DNA synthesis inhibitor is a pyrimidine nucleoside analog (e.g., capecitabine, cytarabine, decitabine, floxuridine, gemcitabine, vidaza, 5-fluorouracil). In some embodiments, the DNA synthesis inhibitor is cladribine. In some embodiments, the DNA synthesis inhibitor is clofarabine. In some embodiments, the DNA synthesis inhibitor is fludarabine. In some embodiments, the DNA synthesis inhibitor is nelarabine. In some embodiments, the DNA synthesis inhibitor is pentostatin. In some embodiments, the DNA synthesis inhibitor is tiazofurin. In some embodiments, the DNA synthesis inhibitor is 6-mercaptopurine. In some embodiments, the DNA synthesis inhibitor is 6-thioguanine. In some embodiments, the DNA synthesis inhibitor is capecitabine. In some embodiments, the DNA synthesis inhibitor is cytarabine. In some embodiments, the DNA synthesis inhibitor is decitabine. In some embodiments, the DNA synthesis inhibitor is floxuridine. In some embodiments, the DNA synthesis inhibitor is vidaza. In some embodiments, the DNA synthesis inhibitor is 5-fluorouracil.

[0189] In some embodiments, the DNA synthesis inhibitor is gemcitabine (2’,2’-difluoro-2’-deoxycytidine (dFdC)), or a pharmaceutically acceptable salt thereof. Gemcitabine is a nucleoside analog that acts to inhibit DNA synthesis. Gemcitabine has been used as monotherapy, in various combinations, and on various dosing schedules in a plurality of solid tumors and hematologic malignancies. Upon administration, gemcitabine is actively taken up by cells via transporters and then phosphorylated by the rate-limiting enzyme deoxycytidine kinase to form gemcitabine monophosphate (dFdCMP). Two additional phosphates are added by other enzymes, converting dFdCMP to gemcitabine diphosphate (dFdCDP) and gemcitabine triphosphate (dFdCTP). Gemcitabine diphosphate inhibits ribonucleotide reductase (RRM), depleting the cellular dNTP pool. This depletion of cellular deoxynucleotides (particularly dCTP) favors the incorporation of dFdCTP into DNA over dCTP. The incorporation of dFdCTP into DNA inhibits further DNA synthesis, resulting in masked strand ends. Figure 4 shows the above mechanism. See, for example, Alvarellos et al., Pharmacogenet. Genomics, 24:564-574 (2014). Gemcitabine is a compound having the following structure. [Chemical Formula]

[0190] In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not SRA737. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not LY2880070. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not V158411. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not CASC-578. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not rubelcetib. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not AZD7762. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not MK8776. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not PF-00477736. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not GDC-0575. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not XL-844.

[0191] In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not adavosertib. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not azenosertib (Zn-C3). In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not NUV-569.

[0192] In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not belvosertib. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not elimusertib. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not seralasecib. In some embodiments, the DNA synthesis inhibitor is gemcitabine and the therapeutic agent is not camonsertib (RP-3500).

[0193] In some embodiments, the DNA synthesis inhibitor is a DNA damaging agent (e.g., a platinum-based anti-cancer agent (e.g., cisplatin and carboplatin), an alkylating agent (e.g., temozolomide (TMZ)), and ionizing radiation. In some embodiments, the DNA synthesis inhibitor is a platinum-based anti-cancer agent. In some embodiments, the DNA synthesis inhibitor is cisplatin. In some embodiments, the DNA synthesis inhibitor is carboplatin. In some embodiments, the DNA synthesis inhibitor is an alkylating agent. In some embodiments, the DNA synthesis inhibitor is temozolomide. In some embodiments, the DNA synthesis inhibitor is ionizing radiation.

[0194] In some embodiments, the DNA synthesis inhibitor is a topoisomerase inhibitor. In some embodiments, the DNA synthesis inhibitor is a topoisomerase I inhibitor and / or a topoisomerase II inhibitor. In some embodiments, the DNA synthesis inhibitor is a topoisomerase I inhibitor (e.g., camptothecin or its derivatives (e.g., topotecan, irinotecan, and belotecan), indenoisoquinoline (e.g., indotecan and indimitecan), phenanthridine or its derivatives (e.g., topoval), and indolocarbazole). In some embodiments, the DNA synthesis inhibitor is a topoisomerase II inhibitor (e.g., anthracyclines (e.g., doxorubicin, daunorubicin, epirubicin, and idarubicin), etoposide, and teniposide). In some embodiments, the DNA synthesis inhibitor is camptothecin or its derivative. In some embodiments, the DNA synthesis inhibitor is topotecan. In some embodiments, the DNA synthesis inhibitor is irinotecan. In some embodiments, the DNA synthesis inhibitor is belotecan. In some embodiments, the DNA synthesis inhibitor is indenoisoquinoline. In some embodiments, the DNA synthesis inhibitor is indotecan. In some embodiments, the DNA synthesis inhibitor is indimitecan. In some embodiments, the DNA synthesis inhibitor is phenanthridine or its derivative. In some embodiments, the DNA synthesis inhibitor is topoval. In some embodiments, the DNA synthesis inhibitor is indolocarbazole. In some embodiments, the DNA synthesis inhibitor is anthracycline. In some embodiments, the DNA synthesis inhibitor is doxorubicin. In some embodiments, the DNA synthesis inhibitor is daunorubicin. In some embodiments, the DNA synthesis inhibitor is epirubicin. In some embodiments, the DNA synthesis inhibitor is idarubicin. In some embodiments, the DNA synthesis inhibitor is etoposide. In some embodiments, the DNA synthesis inhibitor is teniposide.

[0195] In some embodiments, the DNA synthesis inhibitor is an antimetabolite of folic acid (e.g., methotrexate and pemetrexed). In some embodiments, the DNA synthesis inhibitor is methotrexate. In some embodiments, the DNA synthesis inhibitor is pemetrexed.

[0196] Region of interest in a biological sample In some embodiments, the biological sample of the subject analyzed with respect to the response prediction signature is tissue from a tumor biopsy sample of the subject. Within the tumor biopsy sample, there is one or more regions of interest (ROIs) in which biomarkers are determined. In some embodiments, the ROI is or includes tumor cells. In some embodiments, the ROI is or includes one or more cell types within the microenvironment surrounding the tumor cells, such as stromal cells, mesenchymal cells, vascular cells, macrophages, etc. In some embodiments, the ROI is or includes cells within the tumor or the microenvironment surrounding the tumor, such as cells positive for a marker specific to a particular cell type (other than the biomarker used to determine the response prediction signature), such as KRAS-positive cells, a fraction of cells positive for a particular cell surface marker. In some embodiments, the ROI is or includes an intracellular region (e.g., nucleus, cytoplasm, etc.). Such regions or cells in the biological sample can be identified by various established methods, including, for example, using pattern recognition software to identify specific ROIs based on tissue morphology or staining with antibodies specific to one or more markers specific to the cell type(s), and then determining the biomarker only in predefined ROIs or labeled cells, or by separating such labeled cells from other cells prior to determining the biomarker there.

[0197] Detection of response prediction signature Methods for detecting the presence of a response prediction signature can be carried out according to methods known in the art. In some embodiments, the present disclosure includes determining whether a subject is a responder or non-responder to a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof by analysis of a tissue sample from the subject. In some embodiments, the tissue sample is a sample of the subject's tumor. In some embodiments, the tissue sample is analyzed to determine whether the tissue sample exhibits a response prediction signature that includes a first, second, and / or third biomarker as described herein.

[0198] In some embodiments, the response prediction signature described herein is detected by contacting a sample from a subject (e.g., a tissue sample, e.g., a tumor sample) with an antibody specific for each of the first, second, and third biomarkers. In some embodiments, the response prediction signature is determined for each of the first, second, and third biomarkers by a multiplex protein-based assay (e.g., immunofluorescence). As described herein, if the amount or level of a biomarker exceeds (or falls below for a third biomarker that is considered positive if the amount or level of the biomarker is below a threshold) the amount or level set by the response prediction signature, the patient is determined to be a responder; otherwise, the patient is considered a non-responder.

[0199] This technique is surprisingly sensitive in that it does not require a large number of total cells from a sample to determine whether a subject is responsive. For example, in some embodiments, the sample to be analyzed contains at least about 50 cells. In some embodiments, the sample to be analyzed contains at least about 100 cells. In some embodiments, the sample to be analyzed contains at least about 200 cells. In some embodiments, the sample to be analyzed contains at least about 300 cells. In some embodiments, the sample to be analyzed contains at least about 400 cells. In some embodiments, the sample to be analyzed contains at least about 500 cells. In some embodiments, the sample to be analyzed contains at least about 600 cells. In some embodiments, the sample to be analyzed contains at least about 700 cells. In some embodiments, the sample to be analyzed contains at least about 800 cells. In some embodiments, the sample to be analyzed contains at least about 900 cells. In some embodiments, the sample to be analyzed contains at least about 1000 cells. In some embodiments, the sample to be analyzed contains from about 200 to about 300 cells.

[0200] Prediction method In some embodiments, the present disclosure provides methods and systems for determining and / or confirming a response to a Chk1 inhibitor (e.g., prexasertib), a Wee1 inhibitor, an ATR inhibitor, or a combination thereof. In some embodiments, the presence of a biomarker is measured from cells taken from a subject suffering from a disease, disorder, or condition described herein. In some embodiments, the cells taken from the subject are tumor cells.

[0201] In some embodiments, the present disclosure provides methods for determining and / or confirming a subject's response to a Chk1 inhibitor (e.g., prior to administration of a therapy). In some embodiments, the present disclosure provides methods for determining and / or confirming a subject's response to a Wee1 inhibitor (e.g., prior to administration of a therapy). In some embodiments, the present disclosure provides methods for determining and / or confirming a subject's response to an ATR inhibitor (e.g., prior to administration of a therapy). Such methods are useful, for example, for predicting whether a subject is a responder or non-responder to a particular therapy prior to administration of that therapy, and for enabling a practitioner to determine the best course of treatment prior to administration of a single dose. In some embodiments, the present disclosure provides methods and systems for confirming a suspected response or non-response to a particular therapy in a subject who has received the particular therapy.

[0202] In some embodiments, the subject to be treated has been previously treated with an anti-cancer agent. In some embodiments, the methods or systems described herein further comprise administering a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof in combination with a second anti-cancer agent. In some embodiments, the second anti-cancer agent is a DDR inhibitor. In some embodiments, the second anti-cancer agent is selected from veliparib (ABT-888), senaparib (IMP4297), eprenetapopt (APR-246), stenoparib, fluzoparib (HS10160), adavosertib (AZD1775), guadecitabine, berzosertib, fadraciclib (CYC065), SRA737, P53MVA, LY3143921, NOV1401, NOV1402, NMS-293, TSL1502, olaparib, MEDI0457, aprea-solid tumor-5, cetuximab, and cisplatin. In some embodiments, the second anti-cancer agent is selected from azenocertib, BBI-355, PEP07, SPH-6162, ZSY-4835, ART0380, and ATG018. In some embodiments, the second anti-cancer agent is a DNA synthesis inhibitor (e.g., gemcitabine).

[0203] Treatment Methods and Treatment Monitoring Targeted therapies in cancer have revolutionized the management of various types of diseases, including, for example, chronic myeloid leukemia, BRAF - mutated melanoma, HER2 - amplified breast cancer, certain thyroid cancers, and non - small cell lung cancer (NSCLC). Despite these well - documented successes, targeted therapies may benefit only subsets of patients, and it has become clear that even among initial responders, resistance can develop over time.

[0204] A great deal of research effort continues to be devoted to predicting which patients will respond to a particular therapy or develop resistance, especially in the field of cancer therapy. However, for many therapies, patients must take a "wait - and - see" approach, in which clinical features are monitored over time before a determination of response or non - response is made. In the case of cancer patients, treatment time is of great importance, and there is not always an option to wait and confirm whether a therapy is effective. Furthermore, cancer clinical trials have undergone several important paradigm shifts to embrace the era of high - precision oncology driven by biomarkers. By integrating appropriate biomarker information, trial designers can appropriately select or at least increase the proportion of the trial cohort of patients most likely to benefit from a particular therapy.

[0205] One such therapy is prexasertib, an inhibitor of checkpoint kinases 1 and 2 (Chk1 and Chk2). Prexasertib has been shown to induce DNA damage and apoptosis in cancer cells in pre - clinical studies and anti - cancer activity in patients with progressive solid tumors in phase I clinical trials. Lowery et al., Clin.Cancer.Res., 23(15):4354 - 4363(2017); Hong et al., J.Clin.Oncol., 34(15):1764 - 1771(2016).

[0206] The present disclosure provides, inter alia, the insight that certain combinations of biomarkers can be used to predict response to prexasertib. The biomarker combinations described herein provide superior predictive power over individual biomarkers or other combinations (e.g., two biomarkers). Such insight allows patients to have the opportunity to use therapies that can extend their lifespan or, otherwise, to seek alternative therapies that are helpful in other ways. Accordingly, the present disclosure provides methods and systems for treating a disease, disorder, or condition, comprising administering a therapy to a subject determined to be responsive to a Chk1 inhibitor (e.g., prexasertib), a Wee1 inhibitor, an ATR inhibitor, or a combination thereof.

[0207] Furthermore, the present disclosure provides techniques for monitoring a therapy for a given subject or cohort of subjects. For example, in some embodiments, repeated monitoring over time enables or achieves the detection of one or more changes in the gene expression or characteristics of the subject that may affect the ongoing treatment regimen. In some embodiments, a change is detected depending on whether a particular therapy administered to the subject is continued, changed, or interrupted. In some embodiments, the therapy may be changed, for example, by increasing or decreasing the dosing frequency and / or amount of one or more drugs or treatments that the subject is already being treated with. Alternatively, or additionally, in some embodiments, the therapy may be changed by adding a therapy using one or more novel drugs or treatments. In some embodiments, the therapy may be changed by interrupting or discontinuing one or more particular drugs or treatments. In some embodiments, monitoring includes quantifying or analyzing changes in the subject's response prediction signature and changing the treatment accordingly.

[0208] Systems and Architectures FIG. 6 illustrates and describes an implementation of a network environment 400 for use in providing the systems, methods, and architectures described herein. Briefly overviewing with reference to FIG. 6 here, a block diagram of an exemplary cloud computing environment 400 is shown and described. The cloud computing environment 400 may include one or more resource providers 402a, 402b, 402c (collectively, 402). Each resource provider 402 may include computing resources. In some embodiments, the computing resources may include any hardware and / or software used to process data. For example, the computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some embodiments, exemplary computing resources may include application servers and / or databases with storage and search capabilities. Each resource provider 402 may be connected to any other resource provider 402 within the cloud computing environment 400. In some embodiments, the resource providers 402 may be connected via a computer network 408. Each resource provider 402 may be connected via the computer network 408 to one or more computing devices 404a, 404b, 404c (collectively, 404).

[0209] The cloud computing environment 400 may include a resource manager 406. The resource manager 406 may be connected to a resource provider 402 and a computing device 404 via a computer network 408. In some embodiments, the resource manager 406 can facilitate the provision of computing resources by one or more resource providers 402 to one or more computing devices 404. The resource manager 406 can receive requests for computing resources from a particular computing device 404. The resource manager 406 can identify one or more resource providers 402 that can provide the computing resources requested by the computing device 404. The resource manager 406 can select a resource provider 402 to provide the computing resources. The resource manager 406 can facilitate the connection between the resource provider 402 and a particular computing device 404. In some embodiments, the resource manager 406 can establish a connection between a particular resource provider 402 and a particular computing device 404. In some embodiments, the resource manager 406 can redirect a particular computing device 404 to a particular resource provider 402 having the requested computing resources.

[0210] FIG. 7 shows examples of a computing device 500 and a mobile computing device 550 that can be used to implement the techniques described in this disclosure. The computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, and other similar computing devices. The components shown here, their connections and relationships, and their functions are meant to be exemplary only, and are not meant to be limiting.

[0211] Computing device 500 includes a processor 502, a memory 504, a storage device 506, a high-speed interface 508 that connects to the memory 504 and a plurality of high-speed expansion ports 510, and a low-speed interface 512 that connects to the low-speed expansion port 514 and the storage device 506. Each of the processor 502, the memory 504, the storage device 506, the high-speed interface 508, the high-speed expansion ports 510, and the low-speed interface 512 is interconnected using various buses and may be mounted on a common motherboard or in other manners as required. The processor 502 processes instructions for execution within the computing device 500, including instructions stored in the memory 504 or the storage device 506, and displays graphic information on a GUI on an external input / output device such as a display 516 coupled to the high-speed interface 508. In other embodiments, multiple memories and types of memories may be used, along with multiple processors and / or multiple buses as required. Also, multiple computing devices may be connected such that each device provides a portion of the required operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Thus, when the terms are used herein to describe that multiple functions are performed by a "processor", this includes embodiments in which the multiple functions are performed by any number of processors (one or more) of any number of computing devices (one or more). Further, when one function is described as being performed by a "processor", this includes embodiments in which the function is performed by any number of processors (one or more) of any number of computing devices (one or more) (e.g., in a distributed computing system).

[0212] The memory 504 stores information within the computing device 500. In some embodiments, the memory 504 is one or more volatile memory units. In some embodiments, the memory 504 is one or more non-volatile memory units. The memory 504 may also be another form of computer-readable medium, such as a magnetic disk or an optical disk.

[0213] The memory device 506 can provide a large-capacity storage device for the computing device 500. In some embodiments, the memory device 506 may be a computer-readable medium such as a floppy (registered trademark) disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid-state memory device, or an array of devices including a device in a storage area network or other configuration, or may include a computer-readable medium. Instructions can be stored in an information carrier. When executed by one or more processing devices (e.g., the processor 502), the instructions perform one or more of the methods as described above. The instructions can also be stored by one or more storage devices such as a computer-readable medium or a machine-readable medium (e.g., the memory 504, the memory device 506, or the memory on the processor 502).

[0214] The high-speed interface 508 manages bandwidth-intensive operations for the computing device 500, while the low-speed interface 512 manages lower bandwidth-intensive operations. Such an assignment of functions is merely an example. In some embodiments, the high-speed interface 508 is coupled to a high-speed expansion port 510 that can receive the memory 504, the display 516 (e.g., via a graphics processor or an accelerator), and various expansion cards (not shown). In an embodiment, the low-speed interface 512 is coupled to the memory device 506 and the low-speed expansion port 514. The low-speed expansion port 514, which may include various communication ports (e.g., USB, Bluetooth (registered trademark), Ethernet (registered trademark), wireless Ethernet (registered trademark)), can be connected to one or more input / output devices such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or a router, for example, through a network adapter.

[0215] As shown in the figure, computing device 500 can be implemented in several different forms. For example, it can be implemented as a standard server 520, or multiple times in a group of such servers. Additionally, it can be implemented in a personal computer such as a laptop computer 522. It can also be implemented as part of a rack server system 524. Alternatively, components from computing device 500 can be combined with other components within a mobile device (not shown) such as mobile computing device 550. Each of such devices can include one or more of computing device 500 and mobile computing device 550, and the overall system can be composed of multiple computing devices that communicate with each other.

[0216] Mobile computing device 550 includes, among other components, a processor 552, a memory 564, input / output devices such as a display 554, a communication interface 566, and a transceiver 568. Mobile computing device 550 may also include a storage device such as a microdrive or other device to provide additional storage. Each of the processor 552, the memory 564, the display 554, the communication interface 566, and the transceiver 568 are interconnected using various buses, and some components can be mounted on a common motherboard or in other manners as required.

[0217] Processor 552 can execute instructions within mobile computing device 550, including instructions stored in memory 564. Processor 552 can be implemented as a chipset of chips including a plurality of separate analog and digital processors. Processor 552 can be provided for coordinating other components of mobile computing device 550, such as, for example, control of the user interface, applications executed by mobile computing device 550, and wireless communication by mobile computing device 550.

[0218] Processor 552 can communicate with a user via control interface 558 and display interface 556 coupled to display 554. Display 554 can be, for example, a TFT (Thin Film Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other suitable display technology. Display interface 556 can include appropriate circuitry for driving display 554 to present graphics and other information to the user. Control interface 558 can receive commands from the user and convert them for submission to processor 552. Additionally, external interface 562 can provide communication with processor 552 to enable short-range communication between mobile computing device 550 and other devices. External interface 562 can provide, for example, wired communication in some embodiments, or wireless communication in other embodiments, and multiple interfaces can also be used.

[0219] Memory 564 stores information within mobile computing device 550. Memory 564 can be implemented as one or more of one or more computer-readable media, one or more volatile memory units, or one or more non-volatile memory units. Extended memory 574 can also be provided to and connected to mobile computing device 550 via an expansion interface 572 that can include, for example, a SIMM (Single In-line Memory Module) card interface. Extended memory 574 can provide additional storage space for mobile computing device 550 or can also store applications or other information for mobile computing device 550. Specifically, extended memory 574 can include instructions for executing or supplementing the above processes and can also include secure information. Thus, for example, extended memory 574 can be provided as a security module for mobile computing device 550 and can be programmed with instructions that enable secure use of mobile computing device 550. Additionally, secure applications can be provided via the SIM card, along with additional information, such as placing identification information on the SIM card in a non-hackable manner.

[0220] The memory can include, for example, flash memory and / or NVRAM memory (non-volatile random access memory) as described below. In some embodiments, the instructions are stored on an information carrier. In some embodiments, when the instructions are executed by one or more processing devices (e.g., processor 552), they execute one or more methods, such as the methods described above. The instructions can also be stored in one or more storage devices, such as one or more computer-readable media or machine-readable media (e.g., memory 564, extended memory 574, or memory on processor 552). In some embodiments, the instructions can be received as a propagated signal, for example, via transceiver 568 or external interface 562.

[0221] The mobile computing device 550 can communicate wirelessly via a communication interface 566 that may include a digital signal processing circuit as needed. The communication interface 566 can provide communication under various modes or protocols, such as, among others, GSM (registered trademark) voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (registered trademark) (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service). Such communication can be performed, for example, using radio frequencies through a transceiver 568. Additionally, short - range communication can be performed using Bluetooth (registered trademark), Wi - Fi (trademark), or other such transceivers (not shown). Additionally, a GPS (Global Positioning System) receiver module 570 can provide additional navigation and location - related wireless data to the mobile computing device 550 that can be appropriately used by applications running on the mobile computing device 550.

[0222] The mobile computing device 550 can also communicate aurally using an audio codec 560 that can receive voice information from a user and convert it into usable digital information. The audio codec 560 can similarly generate audible sound for the user, for example, through a speaker within the handset of the mobile computing device 550. Such sound can include sound from a voice call, can include recorded sound (such as a voice message, a music file, etc.), and can also include sound generated by an application operating on the mobile computing device 550.

[0223] As shown in the figure, the mobile computing device 550 can be implemented in several different forms. For example, it can be implemented as a mobile phone 580. It can also be implemented as part of a smartphone 582, a personal digital assistant, or other similar mobile devices.

[0224] The various embodiments of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include an implementation in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0225] These computer programs (also known as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented in high-level procedural and / or object-oriented programming languages, and / or in assembly / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus, and / or device (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0226] To interact with a user, the systems and techniques described herein can be implemented on a computer, which can include a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, as well as a keyboard and a pointing device (e.g., a mouse or trackball) by which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user. For example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and the input from the user can be received in any form including acoustic, speech language, or tactile input.

[0227] The systems and techniques described herein can be implemented in a computing system. Such a computing system can include a back-end component (e.g., as a data server), or a middleware component (e.g., an application server), or a front-end component (e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0228] A computing system may include clients and servers. The clients and servers are generally far apart from each other and typically communicate through a communication network. The relationship between the client and the server is created by computer programs that operate on their respective computers and have a relationship between the client and the server with each other.

[0229] In some embodiments, the modules described herein may be separated, combined, or incorporated into single or combined modules. The modules shown in the figures are not intended to limit the systems described herein to the software architectures shown in the figures.

[0230] Elements of different embodiments described herein can be combined to form other embodiments not specifically described above. The elements may be excluded from the processes, computer programs, databases, etc. described herein without adversely affecting their operations. Further, the illustrated logical flows do not require the specific order or sequential order shown to achieve the desired results. To perform the functions described herein, various separate elements may be combined into one or more individual elements. In some embodiments, considering the structure, function, and apparatus of the systems and methods described herein.

[0231] The claimed invention's systems, architectures, devices, methods, and processes are considered to include modifications and adaptations developed using information from the embodiments described herein. It is intended that modifications and / or alterations be made to the systems, architectures, devices, methods, and processes described herein.

[0232] Throughout this specification, where an article, device, system, and architecture are described as having, including, or comprising certain components, or where a process and method are described as having, including, or comprising certain steps, it is contemplated that there further exist articles, devices, systems, and architectures of the invention that consist essentially of, or consist of, the recited components, and that there further exist processes and methods according to the invention that consist essentially of, or consist of, the recited process steps.

[0233] Of course, the order of steps or the order of performing certain acts is not important so long as the invention is practicable. Further, two or more steps or acts may be performed simultaneously.

[0234] Any mention of a publication herein (e.g., in the "Background" section) is not an admission that the publication is prior art with respect to any of the claims presented herein. The "Background" section is presented for clarity and is not intended as an explanation of prior art with respect to any claim.

[0235] Headings are provided for the convenience of the reader and are not intended to limit the scope of the subject matter described herein by their presence and / or placement.

[0236] Exemplary Embodiments The following numbered embodiments are non-limiting and illustrate certain aspects of the present disclosure. Embodiment 1. A method of treating a disease, disorder, or condition, comprising administering a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway to a subject in whom a tissue sample has been determined to exhibit a response prediction signature, wherein the response prediction signature is (a) A first biomarker score that is equal to or greater than a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; the first biomarker score; (b) A second biomarker score that is equal to or greater than a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and the second biomarker score; and (c) (i) A third biomarker score that is equal to or greater than a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, or total cyclin A2; the third biomarker score; or (ii) A third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker is selected from total SCF, total FBXW7, or total p27; the method comprising one or more of the third biomarker scores. Embodiment 2. A method for treating a disease, disorder, or condition, comprising administering, to a subject determined not to exhibit a response prediction signature to a therapeutic agent, a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway in combination with a DNA synthesis inhibitor, wherein the response prediction signature is (a) a first biomarker score that is equal to or greater than a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, the first biomarker score; (b) a second biomarker score that is equal to or greater than a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, the second biomarker score; and (c) (i) A third biomarker score that is equal to or greater than a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, cyclin E1 phosphorylated at Ser100, cyclin E1 phosphorylated at Ser103, cyclin E1 phosphorylated at Ser387, cyclin E1 phosphorylated at Thr395, nucleophosmin phosphorylated at Thr199, CDC6 phosphorylated at Ser54, CDC6 phosphorylated at Ser74, pan-nuclear CDC6, Treslin phosphorylated at Ser1000, pan Cks1, pan Cks2, or total cyclin A2; or (ii) A third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker is selected from total SCF, total FBXW7, or total p27, the method comprising one or more of the third biomarker scores. Embodiment 3. The method according to embodiment 2, wherein the DNA synthesis inhibitor is gemcitabine. Embodiment 4. A method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of protein expression or activity in the ATR / Chk1 signaling pathway, the method comprising: (a) receiving, by a processor of a computing device, data from the tissue sample of the subject's cells providing a respective level of one or more of a first, a second, and a third biomarker in the tissue sample; (b) receiving, by the processor, corresponding first, second, and third prediction thresholds for each of the first, second, and third biomarkers; (c) using the data received in step (a), calculating, by the processor, first, second, and third biomarker scores from the respective levels of the first, second, and third biomarkers; (d) Using the data received in step (b) and the data calculated in step (c), by the processor, comparing the first, second, and third biomarker scores against the corresponding first, second, and third prediction thresholds to determine the presence or absence of a response prediction signature in the tissue sample; (e) including classifying, by the processor, the subject as responsive or non-responsive to the therapeutic agent based on the presence or absence of the response prediction signature in the tissue sample; The first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; The second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; The third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27, the method. Embodiment 5. The method according to any one of Embodiments 1 to 4, wherein the response prediction signature includes at least two of the first biomarker score, the second biomarker score, and the third biomarker score. Embodiment 6. The method according to any one of Embodiments 1 to 5, wherein the response prediction signature includes each of the first biomarker score, the second biomarker score, and the third biomarker score. Embodiment 7. The method according to any one of Embodiments 1 to 6, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, or third biomarker is positive. Embodiment 8. The method according to any one of Embodiments 1 to 7, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, and third biomarker are positive. Embodiment 9. The method according to Embodiment 7 or 8, wherein the first prediction threshold is cells in which 3% or more of the first biomarker is positive. Embodiment 10. The method according to Embodiment 7 or 8, wherein the first prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the first biomarker is positive. Embodiment 11. The method according to any one of Embodiments 7 to 10, wherein the second prediction threshold is cells in which 3% or more of the second biomarker is positive. Embodiment 12. The method according to any one of Embodiments 7 to 10, wherein the second prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the second biomarker is positive. Embodiment 13. The method according to any one of Embodiments 7 to 12, wherein the third prediction threshold is cells in which 3% or more of the third biomarker is positive. Embodiment 14. The method according to any one of Embodiments 7 to 12, wherein the third prediction threshold is a cell in which 5%, 6%, 7%, 8%, 9%, or 10% of the third biomarker is positive. Embodiment 15. The first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, the third biomarker is cyclin E1 (i.e., total cyclin E1 as described herein), and each of the first prediction threshold, the second prediction threshold, and the third prediction threshold is a cell in which 5%, 6%, 7%, 8%, 9%, or 10% of each of the first biomarker, the second biomarker, and the third biomarker is positive. The method according to Embodiment 8. Embodiment 16. The method according to any one of Embodiments 1 to 6, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from a weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample. Embodiment 17. The method according to Embodiment 16, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from a weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample. Embodiment 18. A method of treating a disease, disorder, or condition, comprising administering a therapeutic agent, which is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, to a subject in whom it has been determined that the tissue sample exhibits a response prediction signature. The response prediction signature includes a composite score equal to or higher than a composite threshold, and the composite score is (a) A first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; the first biomarker score; (b) A second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; and the second biomarker score; and (c) A third biomarker score, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, and total cyclin A2; the method comprising two or more of the third biomarker score. Embodiment 19. A method of treating a disease, disorder, or condition, comprising combining a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway with a DNA synthesis inhibitor, and administering the combination to a subject determined to not exhibit a response prediction signature to the therapeutic agent, the response prediction signature comprises a composite score above a composite threshold, and the composite score is (a) A first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; the first biomarker score; (b) A second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; the second biomarker score; and (c) A third biomarker score, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, and total cyclin A2; the method comprising two or more of the third biomarker scores. Embodiment 20. The method according to embodiment 19, wherein the DNA synthesis inhibitor is gemcitabine. Embodiment 21. A method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, the method comprising: (a) Receiving, by a processor of a computing device, data from the tissue sample of the subject's cells that provides respective levels of one or more of a first, a second, and a third biomarker in the tissue sample; (b) Receiving, by the processor, a composite threshold; (c) Using the data received in step (a), calculating, by the processor, first, second, and third biomarker scores from the respective levels of the first, the second, and the third biomarker; (d) Using the data calculated in step (c), calculating, by the processor, a composite score from the first, the second, and the third biomarker scores; (e) Using the data received in step (b) and the data calculated in step (d), comparing, by the processor, the composite score to the composite threshold to determine the presence or absence of a response prediction signature in the tissue sample; (f) Classifying, by the processor, the subject as responsive or non-responsive to the therapeutic agent based on the presence of the response prediction signature in the tissue sample or based on the absence of the response prediction signature in the tissue sample, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; The second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, The third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27, the method. Embodiment 22. The composite score includes each of the first biomarker score, the second biomarker score, and the third biomarker score, the method according to any one of Embodiments 18 to 21. Embodiment 23. The composite score is the sum of each of the biomarker scores, and optionally, the biomarker scores are weighted differently before summing, the method according to any one of Embodiments 18 to 22. Embodiment 24. At least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells positive for the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample, the method according to any one of Embodiments 18 to 23. Embodiment 25. The method according to any one of Embodiments 18 to 24, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells positive for the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample. Embodiment 26. The method according to any one of Embodiments 18 to 25, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample. Embodiment 27. The method according to Embodiment 26, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample. Embodiment 28. The method according to any one of Embodiments 1 to 27, wherein the disease, disorder, or condition is related to Chk1, Wee1, ATR, or a combination thereof. Embodiment 29. The method according to any one of Embodiments 1 to 28, wherein the disease, disorder, or condition is cancer. Embodiment 30. The method according to Embodiment 29, wherein the cancer is characterized by a solid tumor. Embodiment 31. The method according to Embodiment 29, wherein the cancer is selected from ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer (small cell and non-small cell), pancreatic cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, and sarcoma. Embodiment 32. The method according to Embodiment 31, wherein the cancer is ovarian cancer. Embodiment 33. The method according to Embodiment 32, wherein the ovarian cancer is high-grade serous ovarian cancer. Embodiment 34. The method according to any one of Embodiments 1 to 33, wherein the disease, disorder, or condition is related to an oncogenic virus. Embodiment 35. The method according to any one of Embodiments 1 to 34, wherein the tissue sample is a tumor biopsy sample. Embodiment 36. The method according to any one of Embodiments 1 to 34, wherein the tissue sample is tumor cells in the tumor biopsy sample. Embodiment 37. The method according to any one of Embodiments 1 to 36, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of the first biomarker, the second biomarker, or the third biomarker in the nuclei of cells in the tissue sample. Embodiment 38. The method according to Embodiment 37, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of the first biomarker, the second biomarker, or the third biomarker in the nuclei of cells in the tissue sample. Embodiment 39. The method according to any one of Embodiments 1 to 38, wherein the therapeutic agent is a Chk1 inhibitor, a Wee1 inhibitor, ATR, or a combination thereof. Embodiment 40. The method according to Embodiment 39, wherein the therapeutic agent is a Chk1 inhibitor. Embodiment 41. The method according to Embodiment 40, wherein the Chk1 inhibitor is prexasertib, SRA-737, PHI-101, LY2880070, V158411, CASC-578, IMP10, SOL-578, or a pharmaceutically acceptable salt of any of the foregoing. Embodiment 42. The method according to Embodiment 41, wherein the Chk1 inhibitor is prexasertib, or a pharmaceutically acceptable salt thereof. Embodiment 43. The method according to Embodiment 42, wherein the Chk1 inhibitor is prexasertib (S)-lactate monohydrate. Embodiment 44. The method according to Embodiment 39, wherein the therapeutic agent is a Wee1 kinase inhibitor. Embodiment 45. The Wee1 kinase inhibitor is selected from adavosertib (AZD1775, MK1775), azenosertib (Zn-C3), Debio0123, STC8123, ATRN-1051, NUV-569, and IMP7068, and the method according to Embodiment 44. Embodiment 46. The therapeutic agent is an ATR inhibitor, and the method according to Embodiment 39. Embodiment 47. The ATR inhibitor is selected from berzosertib (M6620, VX-970), galcitinib (M4344, VX-803), elimusertib (BAY1895344), ceralasertib (AZD6738), M1774, ATRN-119, and camonsertib (RP-3500), and the method according to Embodiment 46. Embodiment 48. The first biomarker is selected from Ser296 phosphorylated Chk or nuclear Chk1, and the method according to any one of Embodiments 1 to 47. Embodiment 49. The first biomarker is Ser296 phosphorylated Chk1, and the method according to Embodiment 48. Embodiment 50. The second biomarker is Ser473 phosphorylated Kap1, and the method according to any one of Embodiments 1 to 49. Embodiment 51. The third biomarker is cyclin E1 (i.e., total cyclin E1 as described herein), and the method according to any one of Embodiments 1 to 50. Embodiment 52. The first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, and the third biomarker is total cyclin E1 (i.e., total cyclin E1 as described herein), and the method according to any one of Embodiments 1 to 51. Embodiment 53. The method according to any one of Embodiments 1 to 52, further comprising administering a second anti-cancer agent to the subject. Embodiment 54. A system for identifying and / or classifying a subject suffering from a disease, disorder, or condition as likely to respond or likely not to respond to a therapy before administering the therapy, the system comprising: a processor; a memory having its instructions, and when the instructions are executed by the processor, the instructions cause the processor to perform one or more steps of the method according to any one of Embodiments 1 to 53.

Examples

[0237] The present disclosure includes descriptions provided in examples that are not intended to limit the scope of any claims. In particular, unless presented in the past tense, what is included in the examples is not intended to mean that the experiments were actually performed. The following non-limiting examples are provided to further illustrate the present disclosure. Those skilled in the art will understand that many changes may be made in the specific embodiments disclosed, and still obtain similar or analogous results without departing from the spirit and scope of the present disclosure.

[0238] Example 1: Prediction of Sensitivity of Human Cancer Cell Lines This example demonstrates the use of the provided response prediction signature to predict the sensitivity of human cancer cell lines to treatment with prexasertib. Previous efforts to identify a response prediction signature have not been successful in predicting sensitivity to prexasertib in human cell lines. In this example, 14 human cancer cell lines were evaluated for three biomarkers, and the existence of a response prediction signature that could accurately predict sensitivity to treatment with prexasertib was confirmed.

[0239] To classify cancer cell lines as predicted responders or non-responders, tissue microarrays (TMAs) generated from cell pellets of human cancer cell lines were stained using antibodies against three biomarkers and imaged using immunofluorescence microscopy. Biomarker 1 (BM1) was Ser296 phosphorylated Chk1. Biomarker 2 (BM2) was Ser473 phosphorylated Kap1. Biomarker 3 (BM3) was total cyclin E1. The cell nuclei were identified using DAPI staining.

[0240] For each cancer cell line, the individual biomarker score was calculated as the percentage of tumor cells in the samples in which the biomarker was positive (Table 1). If the average nuclear signal of the biomarker within the cell (i.e., the average signal intensity of the pixels within the nucleus) was greater than the background threshold, individual tumor cells were classified as positive for the biomarker. For each biomarker, individual tumor cells were assigned to one of 50 bins corresponding to equally spaced average nuclear signal ranges of the biomarker. The background threshold for biomarker 1 was set to include cells with an average nuclear signal corresponding to bin 19 or higher. The background threshold for biomarker 2 was set to include cells with an average nuclear signal corresponding to bin 7 or higher. The background threshold for biomarker 3 was set to include cells with an average nuclear signal corresponding to bin 10 or higher.

[0241] To predict the response status of each cell line, the composite score for each cell line was calculated by using the sum of the values of three individual biomarker scores for the cancer cell lines, with a weight of 1 for biomarker 1 and 3 and a weight of 2 for biomarker 2 (Table 1). For example, (biomarker 1 score) + (2 × biomarker 2 score) + (biomarker 3 score). Human cancer cell lines with a composite score exceeding a composite threshold of 0.9 were predicted to be responders (i.e., sensitive to treatment with prexasertib), and cancer cell lines with a composite score below the composite threshold were predicted to be non-responders (i.e., insensitive to treatment with prexasertib) (Table 1). Those skilled in the art will understand that other values of the composite threshold may also be capable of distinguishing responders from non-responders.

[0242] The predicted response to prexasertib was compared with the experimentally measured sensitivity to prexasertib. The sensitivity to prexasertib was quantified as the concentration of prexasertib that reduced cell viability by 50% when used to treat the cell line (i.e., EC 50 ). EC 50 values of 1 μM or less were considered actual responders, and values exceeding 1 μM were considered actual non-responders. Comparison of the predicted response status based on the provided response prediction signature and the experimentally determined sensitivity indicates that the provided response prediction signature can accurately predict sensitivity to prexasertib treatment (Figure 8 and Table 1).

[0243] Table 1 shows, for each human cancer cell line evaluated, the individual biomarker scores (BM1, BM2, and BM3), composite score, and predicted response status (R = responder, NR = non-responder), the measured sensitivity to prexasertib (EC 50 ), and the actual response status (R = responder, NR = non-responder) based on the measured sensitivity to prexasertib.

Table 1

[0244] The prediction of responders and non-responders can also be achieved by classifying the individual biomarker scores for each human cancer cell line. Individual biomarker scores between 0 and 0.05 (e.g., having 0 to 5% positive cells) can be classified as low (L). Individual biomarker scores between 0.05 and <0.30 (e.g., having 5 to <30% positive cells) can be classified as medium (M). Individual biomarker scores between 0.30 and up to 1.00 (e.g., having 30 to up to 100% positive cells) can be classified as high (H). Responders and non-responders can be predicted based on the score classification of each biomarker (Table 2). To predict non-responders in this way, the score of one or more of the three biomarkers must be in the L category. To predict responders in this way, the scores of all three biomarkers must be in the M or H category. The pattern of biomarker score categories was used to predict the human cancer cell line T47D as a non-responder and OVCAR3 as a responder (Figure 9A). This is consistent with the sensitivity to prexasertib treatment observed for the two cell lines (Figure 9B). These results demonstrate the success of predicting the response to prexasertib in human cancer cell lines using the three biomarkers.

[0245] Table 2 shows the biomarker score categories used to predict responders (R) and non-responders (NR).

Table 2

[0246] Combinations of additional biomarkers also accurately predict the response to prexasertib. For example, using the above procedure in many of the cell lines tested in Table 1, biomarker levels of various BM1 (S317 phosphorylated Chk1, T68 phosphorylated Chk2, and total (pan) Chk1), BM2 (S642 phosphorylated Wee1), and BM3 (cyclin A2) were evaluated. The results for each of these other biomarkers, and the relative sensitivity of each of the cell lines tested, are shown in the heat map of FIG. 9C.

[0247] Example 2: Prediction of Patient Response Frequency across Human Tumors This example demonstrates the use of the provided response prediction signature to predict the patient response frequency to treatment with prexasertib for several human cancer types.

[0248] To determine the predicted patient response frequency for each human cancer type, a tissue microarray (TMA) containing multiple tumor samples for each cancer type was obtained. The tumor samples were manually stained using antibodies against three biomarkers and imaged using immunofluorescence microscopy. Biomarker 1 (BM1) was Ser296 phosphorylated Chk1. Biomarker 2 (BM2) was Ser473 phosphorylated Kap1. Biomarker 3 (BM3) was total cyclin E1. The tumor samples were further stained using an antibody against epithelial cytokeratin to identify and distinguish the tumor cells of interest from the surrounding stroma. Staining with DAPI was also performed to identify cell nuclei.

[0249] For each tumor sample, the individual biomarker score was calculated as the percentage of tumor cells in the samples in which the biomarker was positive. When the average nuclear signal of the biomarker of the cells was greater than the background threshold of the biomarker, the individual tumor cells were classified as positive for the biomarker. The individual biomarker scores could further be binned into high, medium, or low based on the biomarker scores. The individual biomarker scores were considered low between 0 and <0.05, medium between score 0.05 and <0.30, and high between score 0.30 and 1.00.

[0250] Based on the provided response prediction signature, each tumor sample was predicted as either a responder or a non-responder to prexasertib treatment. If each of the individual biomarker scores of the three biomarkers was above the corresponding prediction threshold of that biomarker, the tumor sample was predicted to be a responder (R). If any of the individual biomarker scores was below the corresponding prediction threshold of that biomarker, the tumor sample was predicted to be a non-responder (NR). The prediction thresholds in this non-limiting example for BM1, BM2, and BM3 were 0.05, 0.30, and 0.05, respectively. Those skilled in the art will understand that other values of the prediction threshold may also be able to distinguish responders from non-responders.

[0251] The predicted response frequency for each cancer type was calculated based on the number of tumor samples predicted to be responders, compared to the total number of tumor samples tested for that cancer type (Table 3). In cancer types where clinical trials of prexasertib were conducted, the observed response rate (ORR) in patients correlated with the predicted response frequency, thus demonstrating the success of predicting response rates for various cancer types using the provided response prediction signature (Table 2). Surprisingly, bladder cancer and endometrial cancer, two cancer types for which clinical trials of prexasertib have not been conducted, are predicted to have high response frequencies compared to the other cancer types investigated, suggesting that these cancer types are promising new indications for treatment with prexasertib (Table 3). Furthermore, the ability of the provided response prediction signature to predict response rates across multiple different cancer types indicates that the provided response prediction signature can predict responses to treatment for any cancer. Thus, the biomarkers and the provided response prediction signature described herein define a new method for predicting responses to treatment based on biomarkers without requiring specific information regarding the tissue location or cell origin of the cancer.

[0252] Table 3 shows the results of tests for predicting response frequencies using tumor samples from multiple cancer types. For each cancer type, the total number of tumor samples analyzed, the number of tumor samples classified as predicted responders, and the predicted response frequency are shown. For cancer types in which clinical trials of prexasertib were conducted, the observed response rate (ORR) is provided.

Table 3

[0253] To evaluate the robustness of the predicted response frequencies using the provided response prediction signatures, a second set of tumor samples from each of bladder cancer, endometrial cancer, and primary high-grade serous ovarian cancer was tested. In the second set, an automated staining apparatus was used to prepare the tumor samples instead of the manual staining procedure. The predicted response frequencies (Table 4) for the second set of tumor samples were similar to the response frequencies (Table 3) from the first set of tumor samples, demonstrating that the provided response prediction signature is not affected by the method of sample preparation.

[0254] Table 4 shows the results of tests to predict response frequencies using tumor samples of multiple cancer types. For each cancer type, the total number of tumor samples analyzed, the number of tumor samples classified as predicted responders, and the predicted response frequencies are shown.

Table 4

[0255] Example 3: Prediction of Prexasertib Response in Patient-Derived Xenograft (PDX) Models This example demonstrates the prediction of response to prexasertib treatment using patient-derived xenograft models of ovarian tumors. Samples of 14 ovarian PDX tumors were obtained, stained using antibodies against three biomarkers, and imaged using immunofluorescence microscopy. Biomarker 1 (BM1) was Ser296 phosphorylated Chk1. Biomarker 2 (BM2) was Ser473 phosphorylated Kap1. Biomarker 3 (BM3) was total cyclin E1. Staining with epithelial cytokeratin and DAPI was also performed to identify the tumor region and cell nuclei, respectively. For each PDX model, the individual biomarker score was calculated as the percentage of tumor cells in the sample where the biomarker was positive (Table 5). Individual tumor cells were classified as biomarker positive if the mean nuclear signal of the biomarker in the cells was greater than the background threshold. Based on the provided response prediction signature, the PDX models were predicted to be either responders or non-responders to prexasertib treatment (Table 5). If all of the individual biomarker scores for each of the three biomarkers were above the corresponding prediction threshold for that biomarker, the PDX model was predicted to be a responder (R). If any of the individual biomarker scores were below the corresponding prediction threshold for that biomarker, the PDX model was predicted to be a non-responder (NR). The prediction thresholds for BM1, BM2, and BM3 were 0.051, 0.05, and 0.05, respectively. One of ordinary skill in the art will understand that other values of the prediction threshold may also be able to distinguish responders from non-responders.

[0256] To evaluate whether the provided response prediction signature could predict response to prexasertib treatment, the predicted response for each PDX model was compared to the experimentally determined response to prexasertib treatment for the same PDX model. The experimentally measured response was quantified as follows: when the size / volume of the treated tumor decreased, the percent shrinkage compared to the initial volume of the treated tumor was obtained; when the size / volume of the treated tumor increased, the percent growth of the treated tumor was divided by the growth of the control tumor (i.e., untreated tumor) (d[T / C]). Negative values of the shrinkage rate indicate response to treatment with prexasertib, and positive values indicate continued growth and non-response to prexasertib treatment. The experimentally measured response was further classified as complete shrinkage (nearly -100% shrinkage), partial shrinkage (-50% to -100% shrinkage), stasis (<20% median tumor growth to -50% shrinkage), and growth (>20% median tumor growth). The predicted response correlated with the experimentally determined response (Table 5 and Figure 10), demonstrating that the response prediction signature could accurately predict response to prexasertib treatment in ovarian PDX tumor models.

[0257] Table 5 shows the results of a study to predict response to prexasertib treatment in ovarian PDX tumor models. For each PDX model, the individual biomarker score, predicted response, measured shrinkage rate, and measured response type are shown.

Table 5

[0258] Alternative response prediction signatures for predicting response to prexasertib treatment were also developed from ovarian PDX tumor models based on linear modeling of individual biomarker scores and percent reduction in tumor volume at the time of treatment (e.g., shrinkage rate). To evaluate the performance of this model, receiver operating characteristic (ROC) analysis was performed. This model was determined to be highly predictive with an area under the curve (AUC) of 0.89 and a 95% confidence interval from 0.52 to 1 (Figure 11). This model will enable the selection of a predicted response group in which 64% are responders (approximately 3.5-fold enrichment of responders) while still capturing 80% of all actual responders within the population from a population with a 20% actual response rate (Figure 11). This enrichment of responders is more than twice the observed response rate (ORR) required for a rapid registration trial, demonstrating the success of the biomarker in predicting response to prexasertib treatment.

[0259] Example 4: Prediction of patient responders using fresh frozen clinical tumor samples This example demonstrates the prediction of patient response to prexasertib therapy using pretreatment tumor biopsy sections from patients with metastatic high-grade serous ovarian cancer. This study was performed in a blinded fashion using a pre-designed method on pretreatment tumor biopsy samples obtained from ovarian cancer patients previously treated with prexasertib in a Phase II NCI trial, including a previous single-institution Phase II clinical trial of prexasertib in metastatic high-grade serous ovarian cancer. See Lee et al., Lancet Oncol. 19(2):207-215(2018). In these previous studies, predictive biomarkers could not be identified. To evaluate whether the provided response prediction signature could successfully predict patient responders, frozen, anonymized sections of pretreatment tumor biopsies from the Phase II clinical trial were obtained. Furthermore, they were received without any knowledge of the clinical annotations, including response to prexasertib. Tumor biopsy samples from each patient were stained using antibodies against three biomarkers and imaged using immunofluorescence microscopy. Biomarker 1 (BM1) was Ser296 phosphorylated Chk1. Biomarker 2 (BM2) was Ser473 phosphorylated Kap1. Biomarker 3 (BM3) was total cyclin E1. Unless otherwise indicated, these were the biomarkers used throughout the example.

[0260] To identify and distinguish the target tumor cells from the surrounding stroma, the tumor samples were further stained using an antibody against cytokeratin and stained with DAPI to identify cell nuclei.

[0261] For the tumor biopsy samples of each patient, the individual biomarker scores were calculated as the percentage of tumor cells in the samples in which the biomarker was positive (Table 6). When the average nuclear signal of the biomarker in the cells was greater than the background threshold, the individual tumor cells were classified as positive for the biomarker. For each biomarker, the individual tumor cells were assigned to one of 50 bins corresponding to equally spaced average nuclear signal ranges of the biomarker. The background threshold for Biomarker 1 was set to include cells with an average nuclear signal corresponding to Bin 13 or higher. The background threshold for Biomarker 2 was set to include cells with an average nuclear signal corresponding to Bin 9 or higher. The background threshold for Biomarker 3 was set to include cells with an average nuclear signal corresponding to Bin 10 or higher.

[0262] Based on the provided response prediction signature, each patient was predicted to be either a responder or a non-responder to prexasertib treatment (Table 6). If all of the individual biomarker scores for each of the three biomarkers were above the corresponding prediction threshold for that biomarker, the patient was predicted to be a responder (R). If any of the individual biomarker scores were below the corresponding prediction threshold for that biomarker, the patient was predicted to be a non-responder (NR). The prediction thresholds for BM1, BM2, and BM3 were 0.141, 0.071, and 0.0404, respectively. Those skilled in the art will understand that other values of the prediction threshold may also be able to distinguish responders from non-responders.

[0263] To evaluate whether the provided response prediction signature could predict response to prexasertib treatment, the predicted response for each patient was compared to the treatment outcome observed in a previous Phase II clinical trial conducted on that patient (Table 6 and Figures 5, 12, and 13). This was done blindly, and the quantitative biomarker scores and clinical annotations were provided to a third-party biostatistician and the principal investigator, respectively. The observed treatment outcomes were classified as partial response (PR), stable disease (SD), or progressive disease (PD) according to the RECIST criteria. Notably, all patients who showed a partial response were predicted to be responders, while patients predicted to be non-responders did not show a partial response (Table 6). Additionally, the observed treatment outcomes were quantified based on the rate of change in tumor volume and progression-free survival (PFS) after treatment (Table 6 and Figures 5, 12, and 13). Patients predicted to be responders showed observed treatment outcomes with a substantial reduction in tumor volume (Figure 12) and generally longer PFS (Figures 5 and 13) when treated with prexasertib. Surprisingly, even among patients who showed stable disease after treatment with prexasertib, patients predicted to be responders had a longer PFS than those predicted to be non-responders (Figure 13). These results demonstrate that the provided response prediction signature can accurately predict the response of patients to prexasertib using pretreatment tumor biopsy samples.

[0264] Table 6 shows the results of a study to predict the response of patients to prexasertib treatment using pretreatment tumor biopsy sections from patients with metastatic high-grade serous ovarian cancer. For each patient, the individual biomarker score, predicted response, RECIST classification of treatment outcome, rate of change in tumor volume (FracShrink), and PFS are shown. [Table 6]

[0265] To validate a combination of three biomarkers for use in predicting response to treatment with prexasertib, the ability of individual biomarkers and combinations of two of the three biomarkers to predict response to treatment was evaluated for comparison. For each individual biomarker or combination of biomarkers, patients were predicted to be either responders or non-responders to prexasertib treatment based on a response prediction signature. If the individual biomarker score of each biomarker being investigated was above the corresponding prediction threshold for that biomarker, the patient was predicted to be a responder (Table 7). If any of the individual biomarker scores of the biomarkers being investigated were below the corresponding prediction threshold for that biomarker, the patient was predicted to be a non-responder. The prediction threshold for each biomarker was determined by optimizing to minimize the sum of the number of patients classified as responders showing progressive disease at the time of treatment and the number of patients classified as non-responders showing partial response at the time of treatment. Within the optimal range, the prediction threshold was further optimized based on the difference in the mean value of the maximum reduction in SD.

[0266] Table 7 shows the prediction thresholds used to predict responders and non-responders using combinations of three biomarkers, combinations of two biomarkers, and individually.

Table 7

[0267] The ability of three biomarkers to predict response to treatment, either individually, as a combination of two biomarkers, or as a combination of all three biomarkers, was evaluated using the treatment outcomes observed in the predicted responder and non-responder groups. The prediction using all three biomarkers in combination showed a greater separation in the median survival time after treatment with prexasertib between the predicted responder and non-responder groups than the predictions using the biomarkers individually or in combination of two biomarkers (Figs. 14A - 14G). These results support the synergistic effect of using all three biomarkers in combination, as opposed to using individual biomarkers alone, to predict response to treatment.

[0268] Example 5: Prediction of patient responders using formalin-fixed paraffin-embedded clinical tumor samples This example demonstrates the prediction of patient response to prexasertib therapy using formalin-fixed paraffin-embedded (FFPE) tumor biopsy sections from patients with metastatic high-grade serous ovarian cancer prior to treatment. Biopsy samples from several independent clinical phase 2 trials were integrated for this analysis based on availability. The trial JTJN (NCT03414047) was a large-scale, multi-site, multinational, non-randomized, parallel cohort phase 2 trial in subjects with advanced high-grade serous ovarian cancer, primary peritoneal cancer, or fallopian tube cancer. Additional FFPE samples were provided from several single-site phase 2 trials of NCI-sponsored prexasertib in BRCA1 / 2 wild-type or BRCA1 / 2 mutant recurrent high-grade serous ovarian cancer (NCT02203513). See Lee, Lancet Oncol. 19(2):207-215 (2018). To evaluate whether the provided response prediction signature could accurately predict patient responders, sections of pre-treatment tumor biopsies were stained blindly for treatment outcome using antibodies against three biomarkers: biomarker 1 (BM1) Ser296 phosphorylated Chk1, biomarker 2 (BM2) Ser473 phosphorylated Kap1, and biomarker 3 (BM3) total cyclin E1, and imaged using immunofluorescence microscopy. To identify and distinguish the tumor cells of interest from the surrounding stroma, the tumor samples were further stained with an antibody against cytokeratin and stained with DAPI to identify cell nuclei.

[0269] The individual biomarker scores for each biomarker were calculated as the percentage of tumor cells in samples where the biomarker was positive (Table 8). The quantitative biomarker scores were provided to a third-party biostatistician. Individual tumor cells were classified as biomarker-positive if the average nuclear signal of the biomarker in the cells was greater than the background threshold. For each biomarker, individual tumor cells were assigned to one of 50 bins corresponding to equally spaced average nuclear signal ranges of the biomarker. The background threshold for Biomarker 1 was set to include cells with an average nuclear signal corresponding to bin 8 or higher. The background threshold for Biomarker 2 was set to include cells with an average nuclear signal corresponding to bin 5 or higher. The background threshold for Biomarker 3 was set to include cells with an average nuclear signal corresponding to bin 11 or higher.

[0270] The individual response prediction signatures (Table 8) were used to predict whether a patient was a responder or non-responder to prexasertib treatment. If the individual biomarker score for each of the three biomarkers was above the corresponding prediction threshold for that biomarker, the patient was predicted to be a responder (R). If any of the individual biomarker scores were below the corresponding prediction threshold for that biomarker, the patient was predicted to be a non-responder (NR). The prediction thresholds were derived through logistic regression and linear regression methods, as well as direct optimization, and their performance was evaluated through extensive jackknife training and testing to prevent overfitting, and further evaluated by bootstrap. The prediction thresholds based on the transformation using the logistic function directly model the probability of a patient responding, taking into account the patient's biomarker scores and intensity measurements. The prediction thresholds based on the transformation using the logarithmic function incorporate a certain coefficient of variation distribution of the expected log-normal of the measurements. The prediction thresholds for BM1, BM2, and BM3 were 0.18, 0.08, and 0.11, respectively. Those skilled in the art will understand that other values of the prediction thresholds may also be able to distinguish responders from non-responders.

[0271] The response prediction signature was evaluated by comparing the predicted response to prexasertib for each patient with the response observed in clinical trials (Table 8, Figure 15). The responses from the observed clinical trials were provided separately to an independent biostatistician. They were classified according to the RECIST criteria as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Additionally, the observed treatment outcomes were quantified based on the maximum percent change in tumor volume after treatment (Table 8, Figure 15). Notably, all patients except one who showed a RECIST response (PR or CR) were predicted to be responders, and all patients except one who showed RECIST progressive disease (PD) were predicted to be non-responders. The clinical RECIST responder rate (PR or CR) among patients predicted to be non-responders by the response prediction signature was 5%. The clinical RECIST responder rate (PR or CR) among patients predicted to be responders by the response prediction signature was 58%. This 58% represents a substantial enrichment of responders compared to the overall unenriched response rate of 25%. The response prediction signature also showed statistical significance in identifying patients with increased tumor shrinkage after prexasertib treatment, with a p-value of 0.008 in the Wilcoxon test (Figure 15).

[0272] Table 8 shows the results of a study using pre-treatment tumor biopsy sections from patients with metastatic high-grade serous ovarian cancer to predict the response of patients to prexasertib treatment. For each patient, the individual biomarker score, response prediction signature, RECIST classification of treatment outcome, and percent change in tumor volume (FracShrink) are shown.

Table 8-1

Table 8-2

[0273] Example 6: Knockdown of Biomarkers in Prexasertib-Sensitive Cells Induces Prexasertib Resistance This example provides experimental evidence that when the level of one of biomarkers BM1, BM2, or BM3 is reduced below the predictive threshold level by siRNA knockdown, the resistance to prexasertib increases in specific cells. In this example, the knockdown of BM1 is the knockdown of CHEK2 (encoding Chk2), and thus, as BM1, it represents any one of Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, or Thr387 phosphorylated Chk2. The knockdown of BM2 is the knockdown of TRIM28 (encoding Kap1), and as BM2, it represents the Ser473 phosphorylation of Kap1. The knockdown of BM3 is either (i) the knockdown of CCNE1 (encoding cyclin E1), and thus, as BM3, it represents any one of total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, or Thr395 phosphorylated cyclin E1, or (ii) the knockdown of CCNA2 (encoding cyclin A2), and thus, as BM3, it represents either total cyclin A2.

[0274] As shown in Table 1 above, OVCAR3 cells are responsive to prexasertib. OVCAR3 cells cultured in RPMI medium containing 20% FBS were transfected with siRNAs (Silencer Select, catalog number 4390824, ThermoFisher) targeting TRIM28 (KAP1), CCNE1, CCNA2, or CHEK2 using RNAiMAX transfection reagent (ThermoFisher), compared with negative control siRNA (catalog number 4390843, ThermoFisher). Twenty-four hours after transfection, the cells were reseeded into 384-well plates, allowed to adhere for 24 hours, and then treated with increasing doses of prexasertib. Seventy-two hours after drug treatment, relative cell viability against DMSO control was evaluated using Cell Titer Glo 2.0 (Promega) and a SpectraMax microplate reader. The results are shown in FIGS. 16A - 16D. Error bars represent the standard deviation from four replicate samples. The absolute EC 50 of prexasertib in the BM2 (TRIM28 (KAP1)) and BM3 (CCNE1) siRNA knockdown experiments were > 10 μM and > 0.025 μM, respectively, compared to 0.1 μM and 0.025 μM of cells treated with negative control siRNA in the BM2 (TRIM28 (KAP1)) and BM3 (CCNE1 (cyclin E1)) knockdown experiments, respectively. The absolute EC 50 of prexasertib in the BM1 (CHEK2 (Chk2)) and BM3 (CCNA2 (cyclin A2)) siRNA knockdown experiments were 31 nM and 164 nM, respectively, compared to 12 nM and 24 nM of cells treated with negative control siRNA in the BM1 (CHEK2 (Chk2)) and BM3 (CCNA2 (cyclin A2)) knockdown experiments, respectively.

[0275] Example 7: Determination of Background Threshold of Phosphorylated KAP1 (“Biomarker 2”) and Scoring of Tumor Samples This example shows a method for determining the background threshold level of a biomarker for use in the methods of the present disclosure.

[0276] Intensity measurement value of biomarker 2 A typical data set included multiple tumor samples from different subjects, and multiple images were obtained from each tumor sample. Each tumor sample was stained with a cytokeratin-specific stain for distinguishing tumor cells from non-tumor cells, DAPI (a nucleus-specific stain), and an antibody specific to biomarker 2 (BM2). After uploading these multiplex images from each tumor sample to inForm® tissue analysis software (Akoya Biosciences), the average BM2 intensity of each nucleus in the image was calculated. An example of the readings of this calculation for 12 different cell nuclei in one image of a tumor sample is shown in Table 9 below.

Table 9

[0277] Calculation of bin distribution and bin size inForm software generated a 50-bin frequency distribution for the average nuclear BM2 signal. To determine the bin size as the main distribution parameter, the signal intensity range of the highest-intensity bin (i.e., the 50th bin) was established. This was done by adopting three representative sample images from three separate tumor samples where the overall intensity of BM2 staining was the highest. In each of these sample images, the nuclear signal intensity of the top 2% of cells with the highest BM2 signal was measured. Next, those intensity values were averaged to obtain the threshold of the highest bin. It was 500 intensity units. The size of each bin was 1 / 50 of the 50th bin threshold, or 10 intensity units (i.e., bin 1: 0 - 10 intensity units, bin 2: 11 - 20 intensity units... bin 49: 481 - 490 intensity units, bin 50: >491 intensity units). For each image in the dataset, inForm software calculated the BM2 signal intensity for each nucleus in it and assigned that nucleus to the appropriate bin. This was done for each image of each tumor sample in the dataset. The output obtained from a given image was a table containing the number (numerical value) or percentage (%) of tumor nuclei within each bin. A part of this table representing bins 1 - 10 is shown below (Table 10). Next, for each tumor sample, the combined 50-bin percentage distribution was calculated by combining the 50-bin frequency distributions for each image of that tumor sample.

Table 10

[0278] Determination of background threshold The background threshold was first determined by combining the 50-bin distributions of the three tumor samples with the lowest overall BM2 staining intensity in the dataset ("BM2 lowest") and the three tumor samples with the highest overall BM2 staining intensity ("BM2 highest"). The overall BM2 staining intensity for a tumor sample was calculated by the inForm software as the average intensity of BM2 in all tumor nuclei of all images from that sample. For each bin, to generate the combined distribution, the number of nuclei from each image of the three selected tumor samples included in that bin was summed, and then the percentage of nuclei from those three samples was calculated. The BM2 scores for different bins were calculated as the percentage of all cells present in a given bin and all bins above it. For example, the score for bin 8 was the sum of the percentages of cells in bins 8-50. Table 11 shows the representative bin scores for bins 8-12. Bin 9, which provided the bin score of the "BM2 lowest" sample closest to 1%, was selected as the background threshold bin. For a bin size of 10 intensity units, this corresponded to a background threshold of 80 intensity units.

Table 11

[0279] A similar process was used to determine the background thresholds for other biomarkers useful in the methods disclosed herein.

[0280] Example 8: Dependency of the CHEK1 gene correlates with the dependencies of the WEE1 and ATR genes This example demonstrates that knockdown of WEE1 or ATR by RNAi highly correlates with CHEK1 dependency across hundreds of cell lines screened. Data was plotted by searching for the top co-dependencies of the CHEK1 gene as evaluated by RNAi screening in the DepMap portal (depmap.org / portal). Figure 17A shows a volcano plot of the top 1000 gene dependencies (gray dots) that correlate with CHEK1 dependency across all CCLE (Cancer Cell Line Encyclopedia) RNAi data, highlighting the dependencies of WEE1 and ATR (black dots and annotations). Figure 17B shows scatter plots of CHEK1 RNAi scores that correlate with WEE1 RNAi scores across 669 CCLE cell lines (left panel) and ATR RNAi scores across 710 CCLE cell lines (right panel). Table 12 shows an overview of the top 10 significant gene dependencies, showing the number of cell lines included, the correlation r, the statistical P and Q, and the value of -log10 transformed Q.

Table 12

[0281] Example 9: Inhibitors targeting Chk1, Wee1, and ATR show highly correlated profiles This example demonstrates that the sensitivity profiles of cell lines treated with various Chk1 inhibitors, Wee1 inhibitors, or ATR inhibitors show strong correlations. Figure 18 shows data analyzed for correlations after being obtained from the PRISM Secondary Drug Screen dataset of the DepMap portal (depmap.org / portal). The correlation matrix shows hierarchically clustered correlation significance (-log10 transformed p-values) among the sensitivity profiles of CCLE cell lines to Chk1, Wee1, and ATR inhibitors. The high-correlation profiles of Chk1, Wee1, and ATR inhibitors are highlighted with black-bordered boxes (upper left). In contrast, the sensitivity profile of the PARP inhibitor is shown with distinguishable correlations, which are also highlighted with black-bordered boxes (lower right). The dendrogram shows the distance values on the right side.

[0282] Example 10: Cell lines predicted to be sensitive to Chk1 inhibitors are also sensitive to Wee1 inhibitors This example provides experimental evidence that cell lines predicted to be sensitive to a Chk1 inhibitor (e.g., prexasertib) are also sensitive to Wee1 inhibitors (e.g., adavosertib (AZD1775, MK1775) and azenosertib (Zn-C3)). Cell lines were treated with increasing doses of either prexasertib, adavosertib, or azenosertib, and the relative cell viability compared to the DMSO control was evaluated 72 hours after drug treatment using Cell Titer Glo 2.0 (Promega) and a SpectraMax microplate reader. The results are shown in Figures 19A - 19C. Error bars represent the standard deviation from quadruplicate samples. The cell lines were ranked according to their sensitivity to each drug, and the results are shown in the table below (Table 13).

Table 13

[0283] Example 11: Treatment with gemcitabine enhances the sensitivity of cell lines to treatment with prexasertib This example provides experimental evidence that treatment with gemcitabine enhances the sensitivity of human cancer cell lines to treatment with prexasertib. A panel of human cancer cell lines was treated with various concentrations of prexasertib (0.17, 0.51, 1.5, 4.6, 13.7, 31.2, 123.5, 370.4, 1111, 3333, and 10000 nM) alone or in combination with various low-dose concentrations of gemcitabine (0.53 and 2.7 nM). The panel of cell lines included ovarian cancer, endometrial cancer, bladder cancer, breast cancer, and prostate cancer cell lines. Two ovarian cancer cell lines (OVCAR3 and OV90) were used to generate prexasertib-resistant cells. For both cell lines, the parental cells (OVCAR3-P and OV90-P) as well as the prexasertib-resistant cells (OVCAR3-R and OV90-R) were tested. Additionally, the panel included three cell lines (SW780, T47D, and PC3) that were non-resistant to prexasertib. Three days after treatment, cell viability was evaluated using Cell Titer Glo 2.0 (Promega) compared to the DMSO control. The dose-viability curves for prexasertib treatment alone or in combination with low-dose gemcitabine are shown in FIGS. 20A-20K. The relative and absolute half-maximal inhibitory concentrations (IC 50 ) for prexasertib were determined and are shown in Table 14. The results indicate that treatment with low-dose gemcitabine enhances sensitivity to prexasertib treatment across several cancer cell lines, including prexasertib-resistant cells (OVCAR3-R and OV90-R) and prexasertib-non-resistant cells (SW780, T47D, and PC3).

Table 14

[0284] To determine whether gemcitabine and prexasertib act synergistically, additively, or in some cases antagonistically, a panel of human cancer cell lines was treated with various concentrations of prexasertib (0.17, 0.51, 1.5, 4.6, 13.7, 31.2, 123.5, 370.4, 1111, 3333, and 10000 nM) in combination with various concentrations of gemcitabine (1, 3, 13, 67, 333, 1667, 8333 nM). Three days after treatment, cell viability compared to the DMSO control was evaluated using Cell Titer Glo 2.0 (Promega). The drug combination effect was evaluated using SynergyFinder2.0 (see Ianevski et al., Nucleic Acids Res., gkaa216 (2020)) and is shown in Table 15 as the Bliss synergy effect score for each cell line. A Bliss synergy effect score greater than 10 indicates a synergistic effect between prexasertib and gemcitabine, a score between 10 and -10 indicates an additive effect, and a score less than -10 indicates an antagonistic effect. The results indicate that combination therapy with prexasertib and gemcitabine generally results in a synergistic or additive effect across several cancer cell lines. Furthermore, prexasertib-resistant cells (OVCAR3-R and OV90-R) showed a significant increase in the synergy effect score compared to the parental cells (OVCAR3-P and OV90-P). In summary, the results of this example support the use of a combination of prexasertib and gemcitabine (e.g., low-dose gemcitabine) for the treatment of predicted non-responders to prexasertib therapy.

Table 15

[0285] Example 12: Gemcitabine treatment increases biomarkers predicting prexasertib sensitivity This example provides experimental evidence that treatment with gemcitabine increases biomarkers that predict prexasertib sensitivity. Global pan-proteomic analysis of parental (OVCAR3-P) and prexasertib-resistant acquired (OVCAR3-R) ovarian cancer cells was performed to evaluate changes in protein expression in prexasertib-resistant acquired cells. The results showed that several proteins involved in the ATR activation complex (ATR, ATRIP, and TOPBP1) were not abundant in OVCAR3-R cells (Figure 21), indicating that the ATR-Chk1 signaling axis was lower in OVCAR3-R cells compared to OVCAR3-P cells. These results are consistent with the observation that OVCAR3-R cells are less sensitive to treatment with the Chk1 inhibitor prexasertib (Example 11).

[0286] To determine whether treatment with gemcitabine can change the abundance of biomarkers associated with response prediction signals, global pan-proteomic and phosphoproteomic analyses were performed, comparing treatment with low-dose gemcitabine (10 nM for 24 hours) to DMSO controls in both OVCAR3-P and OVCAR3-R cells. The results showed that gemcitabine treatment significantly increased phosphorylated Chk1 at Ser296, which is the first biomarker (BM1) (Figure 22). Furthermore, the results showed that gemcitabine treatment significantly increased the abundance of total cyclin E1, which is the third biomarker (BM3) (Figure 23). Western blot analysis of the samples was used to verify the increase in the levels of BM1 and BM3 (Figure 24). Additionally, an increase in the level of phosphorylated H2AX, a marker of DNA damage, was detected in cells treated with the combination of gemcitabine and prexasertib compared to treatment with either prexasertib or gemcitabine alone (Figure 24).

[0287] In summary, these results indicate that gemcitabine treatment increased biomarkers (BM1 and BM3) related to response prediction signatures that predict response to prexasertib treatment. Such results support the treatment of predicted non-responders with a combination of gemcitabine (e.g., low dose) and prexasertib.

[0288] Example 13: Prexasertib sensitivity correlates with sensitivity to other Chk1 inhibitors and WEE1 and ATR inhibitors This example demonstrates that sensitivity to prexasertib across multiple cancer cell lines highly correlates with their cell line sensitivity to the Chk1 inhibitors SCH-900776, LY2603618, and AZD7762. It also shows a good correlation between sensitivity to prexasertib and sensitivity to the ATR inhibitor VE-822 and the Wee1 inhibitor adavosertib. The data are from the lineage survival profiling of cancer cell lines and their sensitivities to compounds performed at the Broad Institute [PRISM Repurposing Screen; Corsello et al., Nat Cancer 1(2):235-248 (2020), Yu et al., Nat Biotechnology 34(4):419-423 (2016)]. The data were plotted by searching for the top co-dependencies of prexasertib sensitivity in the DepMap portal (depmap.org / portal). Figure 25 shows the results of this test in a scatter plot.

[0289] Example 14: Mass spectrometry-based phosphoproteomics validates biomarkers predicting prexasertib sensitivity The human ovarian cancer cell lines OVCAR3 (sensitive to prexasertib) and A2780 (prexasertib-insensitive cells) were cultured in RPMI medium supplemented with 20% or 10% FBS, respectively. The cells were seeded in 100 mm Petri dishes in four replicate experiments and incubated for 48 hours. Subsequently, cell cycle synchronization was performed for 24 hours using a 2.5 mM thymidine solution. After synchronization, the plates were carefully rinsed with PBS, and the cells were treated with either DMSO-containing medium (vehicle control) or 100 nM prexasertib for a total of 2.5 hours. The medium was carefully removed, and the plates were rinsed twice with PBS. Cell lysis was then performed by adding a hot (95 °C) lysis buffer containing 100 mM Tris-HCl (pH 8.5), 5% SDS, 5 mM tris(2-carboxyethyl)phosphine (TCEP), and 10 mM 2-chloroacetamide (CAA). The cells were immediately scraped into the lysis buffer, collected in 1.5 mL tubes, and heated at 95 °C for 10 minutes. The samples were sonicated on ice for a total of 1 minute using a microprobe with 1 second on / 1 second off. Protein concentration was measured, and 0.5 mg of protein was used for mass spectrometry sample preparation and phosphorylation peptide enrichment as described, for example, in Bekker-Jensen et al., Molecular & Cellular Proteomics. 19(4):716-729 (2020). Phosphoproteomics data were acquired using an Orbitrap mass spectrometer operated by data-independent acquisition, and the resulting mass spectra were analyzed using the directDIA approach as described in Bekker-Jensen et al., Nature Communications 11(1):787 (2020). The following phosphorylation biomarkers shown in Table 16 below were found to be elevated in the prexasertib-sensitive cell line compared to the prexasertib-insensitive cell line, thus supporting their selection as biomarkers for determining sensitivity to prexasertib (and other Chk1 inhibitors), as well as inhibitors of other proteins correlated with Chk1, such as Chk2, Wee1, and ATR.Table 16 shows the relative abundance (log2 intensity ratio) of phosphorylation sites in prexasertib-sensitive OVCAR3 cells versus non-sensitive A2780 cells (DMSO control sample), along with the adjusted p-values indicated by * when significant (FC 1.5, limma t-test FDR 0.05). log2 intensity ratio > 0 corresponds to phosphorylation sites detected at higher abundance in OVCAR3-sensitive cells compared to A2780 non-sensitive cells in the DMSO control sample. Multiplicity (M1 or M2) indicates whether the phosphorylation site was detected in a peptide phosphorylated singly or doubly. [Table 16] Sequence [Table 18-1] [Table 18-2] [Table 18-3] [Table 18-4] [Table 18-5] [Table 18-6] [Table 18-7] [Table 18-8] [Table 18-9] [Table 18-10] [Table 18-11]

Claims

1. A method of treating a disease, disorder, or condition, comprising administering to a subject determined to have a tissue sample exhibiting a response prediction signature a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, wherein the response prediction signature is a) a first biomarker score greater than or equal to a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, and the first biomarker score; b) a second biomarker score greater than or equal to a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, and the second biomarker score; and c) i) a third biomarker score greater than or equal to a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, or total cyclin A2, and the third biomarker score; or (ii) a third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker comprises one or more of the third biomarker scores, and the third biomarker is selected from total SCF, total FBXW7, or total p27, the method. [

2. ] A method of treating a disease, disorder, or condition, comprising combining a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway with a DNA synthesis inhibitor and administering the combination to a subject in whom a tissue sample has been determined not to exhibit a response prediction signature to the therapeutic agent, wherein the response prediction signature comprises (a) a first biomarker score that is greater than or equal to a first prediction threshold, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, the first biomarker score; (b) a second biomarker score that is greater than or equal to a second prediction threshold, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, the second biomarker score; and (c) (i) A third biomarker score that is equal to or greater than a third prediction threshold, wherein the third biomarker is selected from total cyclin E1, cyclin E1 phosphorylated at Ser100, cyclin E1 phosphorylated at Ser103, cyclin E1 phosphorylated at Ser387, cyclin E1 phosphorylated at Thr395, nucleophosmin phosphorylated at Thr199, CDC6 phosphorylated at Ser54, CDC6 phosphorylated at Ser74, pan-nuclear CDC6, Treslin phosphorylated at Ser1000, pan Cks1, pan Cks2, or total cyclin A2; or (ii) A third biomarker score that is less than or equal to a third prediction threshold, wherein the third biomarker is selected from total SCF, total FBXW7, or total p27, the method comprising one or more of the third biomarker scores.

3. The method according to claim 2, wherein the DNA synthesis inhibitor is gemcitabine.

4. A method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, the method comprising: (a) receiving, by a processor of a computing device, data from the tissue sample of the subject's cells providing the level of each of one or more of a first, a second, and a third biomarker in the tissue sample; (b) receiving, by the processor, corresponding first, second, and third prediction thresholds for each of the first, second, and third biomarkers; (c) using the data received in step (a), calculating, by the processor, first, second, and third biomarker scores from the levels of the first, second, and third biomarkers, respectively; (d) using the data received in step (b) and the data calculated in step (c), comparing, by the processor, the first, second, and third biomarker scores to the corresponding first, second, and third prediction thresholds to determine the presence or absence of a response prediction signature in the tissue sample; (e) classifying, by the processor, the subject as responsive to the therapeutic agent based on the presence of the response prediction signature in the tissue sample or as non-responsive to the therapeutic agent based on the absence of the response prediction signature in the tissue sample, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2; wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1; wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27, the method. **Claim 5** The method according to any one of claims 1 to 4, wherein the response prediction signature comprises at least two of the first biomarker score, the second biomarker score, and the third biomarker score. **Claim 6** The method according to any one of claims 1 to 5, wherein the response prediction signature comprises each of the first biomarker score, the second biomarker score, and the third biomarker score. **Claim 7** The method according to any one of claims 1 to 6, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, or third biomarker is positive.

8. The method according to any one of claims 1 to 7, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, and third biomarker are positive.

9. The method according to claim 7 or 8, wherein the first prediction threshold is cells in which 3% or more of the first biomarker is positive.

10. The method according to claim 7 or 8, wherein the first prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the first biomarker is positive.

11. The method according to any one of claims 7 to 10, wherein the second prediction threshold is cells in which 3% or more of the second biomarker is positive.

12. The method according to any one of claims 7 to 10, wherein the second prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the second biomarker is positive.

13. The method according to any one of claims 7 to 12, wherein the third prediction threshold is cells in which 3% or more of the third biomarker is positive.

14. The method according to any one of claims 7 to 12, wherein the third prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the third biomarker is positive.

15. The method according to claim 8, wherein the first biomarker is phosphorylated Ser296 Chk1, the second biomarker is phosphorylated Ser473 Kap1, the third biomarker is total cyclin E1, and each of the first prediction threshold, the second prediction threshold, and the third prediction threshold is cells in which 5%, 6%, 7%, 8%, 9%, or 10% of the first biomarker, the second biomarker, and the third biomarker are positive.

16. The method according to any one of claims 1 to 6, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample.

17. The method according to claim 16, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample.

18. A method of treating a disease, disorder, or condition, comprising administering a therapeutic agent, which is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, to a subject in whom a tissue sample has been determined to exhibit a response prediction signature, wherein the response prediction signature includes a composite score equal to or greater than a composite threshold, and the composite score is (a) a first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, the first biomarker score; (b) a second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, the second biomarker score; and (c)A third biomarker score, wherein the third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan-nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, and total cyclin A2, and the method comprising two or more of the third biomarker score.

19. A method of treating a disease, disorder, or condition, comprising combining a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway with a DNA synthesis inhibitor and administering the combination to a subject determined not to exhibit a response prediction signature to the therapeutic agent, wherein the response prediction signature comprises a composite score above a composite threshold, and the composite score is (a)A first biomarker score, wherein the first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, and the first biomarker score; (b)A second biomarker score, wherein the second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, and the second biomarker score; and (c)A third biomarker score, wherein the third biomarker is selected from total cyclin E1, cyclin E1 phosphorylated at Ser100, cyclin E1 phosphorylated at Ser103, cyclin E1 phosphorylated at Ser387, cyclin E1 phosphorylated at Thr395, nucleophosmin phosphorylated at Thr199, CDC6 phosphorylated at Ser54, CDC6 phosphorylated at Ser74, pan-nuclear CDC6, Treslin phosphorylated at Ser1000, pan Cks1, pan Cks2, and total cyclin A2, and the method includes two or more of the third biomarker score.

20. The method according to claim 19, wherein the DNA synthesis inhibitor is gemcitabine.

21. A method for identifying and / or classifying a subject as likely to be a responder or non-responder to a therapeutic agent that is an inhibitor of the expression or activity of a protein in the ATR / Chk1 signaling pathway, the method comprising: (a)receiving, by a processor of a computing device, data from the tissue sample of the subject's cells providing the level of each of one or more of a first, a second, and a third biomarker in the tissue sample; (b)receiving, by the processor, a composite threshold; (c)using the data received in step (a), calculating, by the processor, a first, a second, and a third biomarker score from the respective levels of the first, the second, and the third biomarker; (d)using the data calculated in step (c), calculating, by the processor, a composite score from the first, the second, and the third biomarker scores; (e)using the data received in step (b) and the data calculated in step (d), comparing, by the processor, the composite score to the composite threshold to determine the presence or absence of a response prediction signature in the tissue sample; (f)classifying, by the processor, the subject as responsive or non-responsive to the therapeutic agent based on the presence of the response prediction signature in the tissue sample or non-responsive to the therapeutic agent based on the absence of the response prediction signature in the tissue sample. The first biomarker is selected from Ser280 phosphorylated Chk1, Ser296 phosphorylated Chk1, Ser317 phosphorylated Chk1, Ser345 phosphorylated Chk1, nuclear pan Chk1, Thr68 phosphorylated Chk2, Ser516 phosphorylated Chk2, Thr383 phosphorylated Chk2, and Thr387 phosphorylated Chk2, The second biomarker is selected from Ser473 phosphorylated Kap1, Ser642 phosphorylated Wee1, pan Wee1, Ser865 phosphorylated Treslin, Ser743 phosphorylated TLK1, Ser612 phosphorylated RB1, Ser1310 phosphorylated MYBBP1A, Ser508 phosphorylated SETMAR, Thr2252 phosphorylated SRRM2, Ser230 phosphorylated CDC25B, Ser950 phosphorylated claspin, Ser37 phosphorylated FAM122A, Ser151 phosphorylated CDC25B, Ser280 phosphorylated CDC25B, Ser481 phosphorylated FOXM1, and Ser704 phosphorylated FOXM1, The third biomarker is selected from total cyclin E1, Ser100 phosphorylated cyclin E1, Ser103 phosphorylated cyclin E1, Ser387 phosphorylated cyclin E1, Thr395 phosphorylated cyclin E1, Thr199 phosphorylated nucleophosmin, Ser54 phosphorylated CDC6, Ser74 phosphorylated CDC6, pan nuclear CDC6, Ser1000 phosphorylated Treslin, pan Cks1, pan Cks2, total cyclin A2, total SCF, total FBXW7, or total p27, the method as described above.

22. The method according to any one of claims 18 to 21, wherein the composite score includes each of the first biomarker score, the second biomarker score, and the third biomarker score.

23. The method according to any one of claims 18 to 22, wherein the composite score is the sum of each of the biomarker scores, and optionally, the biomarker scores are weighted differently before summation.

24. The method according to any one of claims 18 to 23, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, or third biomarker is positive.

25. The method according to any one of claims 18 to 24, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined as the percentage of cells in the tissue sample in which the corresponding first biomarker, second biomarker, and third biomarker are positive.

26. The method according to any one of claims 18 to 25, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, or third biomarker in the tissue sample.

27. The method according to claim 26, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined from the weighted intensity distribution of the corresponding first biomarker, second biomarker, and third biomarker in the tissue sample.

28. The method according to any one of claims 1 to 27, wherein the disease, disorder, or condition is related to Chk1, Wee1, ATR, or a combination thereof.

29. The method according to any one of claims 1 to 28, wherein the disease, disorder, or condition is cancer.

30. The method according to claim 29, wherein the cancer is characterized by a solid tumor.

31. The method according to claim 29, wherein the cancer is selected from ovarian cancer, anal cancer, cervical cancer, head and neck cancer, esophageal cancer, colon cancer, lung cancer (small cell and non-small cell), pancreatic cancer, liver cancer, bladder cancer, breast cancer, endometrial cancer, and sarcoma.

32. The method according to claim 31, wherein the cancer is ovarian cancer.

33. The method according to claim 32, wherein the ovarian cancer is high-grade serous ovarian cancer.

34. The disease, disorder, or condition is related to an oncovirus, and the method according to any one of claims 1 to 33.

35. The method according to any one of claims 1 to 34, wherein the tissue sample is a tumor biopsy sample.

36. The method according to any one of claims 1 to 34, wherein the tissue sample is tumor cells in the tumor biopsy sample.

37. The method according to any one of claims 1 to 36, wherein at least one of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of the first biomarker, the second biomarker, or the third biomarker in the nucleus of cells in the tissue sample.

38. The method according to claim 37, wherein each of the first biomarker score, the second biomarker score, and the third biomarker score is determined based on detection of the first biomarker, the second biomarker, or the third biomarker in the nucleus of cells in the tissue sample.

39. The method according to any one of claims 1 to 38, wherein the therapeutic agent is a Chk1 inhibitor, a Wee1 inhibitor, an ATR inhibitor, or a combination thereof.

40. The method according to claim 39, wherein the therapeutic agent is a Chk1 inhibitor.

41. The method according to claim 40, wherein the Chk1 inhibitor is prexasertib, SRA-737, PHI-101, LY2880070, V158411, CASC-578, IMP10, SOL-578, or a pharmaceutically acceptable salt of any of the foregoing.

42. The method according to claim 41, wherein the Chk1 inhibitor is prexasertib or a pharmaceutically acceptable salt thereof.

43. The method according to claim 42, wherein the Chk1 inhibitor is prexasertib (S)-lactate monohydrate.

44. The method according to claim 39, wherein the therapeutic agent is a Wee1 kinase inhibitor.

45. The method according to claim 44, wherein the Wee1 kinase inhibitor is selected from adavosertib (AZD1775, MK1775), azenosertib (Zn-C3), Debio0123, STC8123, ATRN-1051, NUV-569, and IMP7068.

46. The method according to claim 39, wherein the therapeutic agent is an ATR inhibitor.

47. The method according to claim 46, wherein the ATR inhibitor is selected from berzosertib (M6620, VX-970), galcitinib (M4344, VX-803), elimusertib (BAY1895344), ceralasertib (AZD6738), M1774, ATRN-119, and camonsertib (RP-3500).

48. The method according to any one of claims 1 to 47, wherein the first biomarker is selected from Ser296 phosphorylated Chk or nuclear Chk1.

49. The method according to claim 48, wherein the first biomarker is Ser296 phosphorylated Chk1.

50. The method according to any one of claims 1 to 49, wherein the second biomarker is Ser473 phosphorylated Kap1.

51. The method according to any one of claims 1 to 50, wherein the third biomarker is total cyclin E1.

52. The method according to any one of claims 1 to 51, wherein the first biomarker is Ser296 phosphorylated Chk1, the second biomarker is Ser473 phosphorylated Kap1, and the third biomarker is total cyclin E1.

53. The method according to any one of claims 1 to 52, further comprising administering a second anti-cancer agent to the subject.

54. A system for identifying and / or classifying a subject having a disease, disorder, or condition as likely to respond or likely not to respond to a therapy before administering the therapy, the system comprising: a processor; a memory having instructions therefor wherein the instructions, when executed by the processor, cause the processor to perform one or more steps of the method according to any one of claims 1 to 53.

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