Cancer prognosis

Abstract

The present application relates to methods of assessing whether a cancer patient is at high or low risk of mortality, and methods of predicting a cancer patient's therapeutic response to an anti-cancer therapy. The methods of the present application can be applied, for example, to selecting patients for clinical trials, selecting patients for treatment with an anti-cancer therapy, monitoring a cancer patient during treatment with an anti-cancer therapy, and assessing the outcome of a clinical trial of an anti-cancer therapy.

Description

TECHNICAL FIELD

[0001] The present invention relates to methods of assessing whether a cancer patient is at high or low risk of death, and methods of predicting the therapeutic response of a cancer patient to an anticancer therapy. The methods of the present invention can be applied, for example, to select patients for clinical trials, to select patients for treatment with an anticancer therapy, to monitor a cancer patient during treatment with an anticancer therapy, and to assess the outcome of a clinical trial of an anticancer therapy. BACKGROUND

[0002] Factors influencing life expectancy are of great importance in public health (Ganna & Ingelsson, 2015). In oncology, predicting patient survival helps to optimize patient management (Halabi & Owzar, 2010). By understanding which variables are prognostic of the outcome, it is possible to gain insight into the disease biology, to personalize treatment to the patient, and perhaps to improve the design, conduct and data analysis of clinical trials.

[0003] Current research on prognostic and predictive factors in oncology is mainly based on small sample sizes. Therefore, mainly one risk factor at a time is investigated for association with death (Banks et al., 2013; Hu et al., 2004; McGee et al., 1999; Thun et al., 1997; Tota-Maharaj et al., 2012). Even existing prognostic scores, such as the Royal Marsden Hospital Score (RMHS) (Nieder & Dalhaug, 2010), the International Prognostic Index (IPI) (N. Engl. J. Med., 329:987-94, 1993), the Glasgow Prognostic Score (GPS) or modified Glasgow Prognostic Score (mGPS) (Kinoshita et al., 2013; Nozoe et al., 2014; Jin et al., 2017; Grose et al., 2014), are constructed from a small number of risk factors, usually less than five. These small sample sizes do not allow for the simultaneous assessment of multiple factors (Altman & Simon, 1994; Graf et al., 1999). Many biomarkers have been proposed in the literature and in research results as prognostic indicators of the death process in cancer patients, and the weaknesses of the reported studies (e.g. small sample size, in particular relative to the number of parameters being assessed, and ongoing univariate rather than multivariate analysis) are summarized in Reid et al., 2017. Despite the high-level evidence reported for some biomarkers, Reid et al., 2017 does not provide evidence that death can be accurately predicted from such biomarkers, and does not describe a model that can be used to make such a prediction.

[0004] Thus, the need for larger sample sizes to be able to obtain more accurate predictions that increase the confidence in clinical decisions is unmet. The recent UK Biobank initiative (Sudlow et al., 2015) is an important addition to the available data and was used by Ganna and Ingelsson (2015) to investigate life expectancy in a population sample of about 500,000 participants and to construct a mortality risk score superior to the Charlson comorbidity index (Charlson et al., 1987). However, given the high impact of cancer on public health (Stock et al., 2018), there is still a need in the art for more accurate tests to predict mortality and treatment response in cancer patients to improve, among others, the treatment of patients, as well as the design, conduct and data analysis of clinical trials. SUMMARY

[0005] The inventors have developed a new method to assess the mortality risk of cancer patients, or to predict the treatment response of cancer patients to anticancer therapies, based on a plurality of parameters. The method stems from the finding that training data comprising routinely measured parameters of a large number of subjects can be used to develop a model that yields more reliable mortality risk and treatment response indicators than currently known scores.

[0006] In particular, the inventors performed survival analysis on 99,249 people from 12 different cohorts (RoPro 1) and 110,538 people from 15 different cohorts (RoPro 2) using data from the Flatiron Health database, the cohorts were defined by tumor type, and validated the results in two independent clinical studies. Demographics and clinical variables (focusing on routinely collected clinical and laboratory data), diagnoses and treatments (Curtis et al., 2018) were examined with real mortality as endpoint, and survival time from first-line treatment was assessed. As mentioned above, the focus was on parameters routinely collected in clinical practice, which makes the method applicable in various settings without the need to specifically collect patient parameter data for the analysis. This is an advantage compared to scores that rely on measurements of parameters that are not routinely measured in the clinic.

[0007] A total of 26 parameters (RoPro1) and 29 parameters (RoPro2) were identified that are routinely measured in cancer patients and have been shown to be able to predict the risk of death in patients with a variety of cancers with much higher accuracy than the Royal Marsden Hospital Score (RMHS), as demonstrated by more accurate prediction of patient length of stay in a Phase I study (BP29428) investigating the safety, pharmacokinetics and preliminary anti-tumor activity of emactuzumab and atezolizumab in patients with selected locally advanced or metastatic solid tumors. The inventors have further shown that using 13 of the 26 parameters of RoPro1 allows prediction of the risk of death with an accuracy approaching that achieved using all 26 parameters, and that using as few as 4 of the 26 parameters is sufficient to predict a patient's risk of death with an accuracy significantly higher than that of the RMHS. The present inventors have similarly shown that using 13 of RoPro2's 29 parameters allows predicting mortality risk with an accuracy approaching that achieved using all 29 parameters and that using as few as 4 of the 29 parameters is sufficient to predict a patient's mortality risk with an accuracy significantly higher than that of RMHS.

[0008] In particular, the present inventors have shown that when patient information corresponding to as few as 4 parameters selected from the following (i) to (xxvi) or (i) to (xxix) is used to calculate RoPro1 (the first 4 parameters: r 2 =0.15; first 5 parameters: r 2 =0.16; first 10 parameters: r 2 =0.17; first 13 parameters: r 2 = 0.19; where top 4, top 5, top 10 and top 13 refer to the parameters ranked 1-4, 1-5, 1-10 and 1-13 of RoPro1 listed in Table 15, respectively) or Ropro2 (the first 4 parameters: r 2 =0.174; first 5 parameters: r 2 =0.184; first 10 parameters: r 2 =0.288; first 13 parameters: r 2 =0.299; where top 4, top 5, top 10, and top 13 refer to the parameters ranked 1-4, 1-5, 1-10, and 1-13 of RoPro2 listed in Table 15, respectively), RoPro1 and RoPro2 were significantly better than RMHS in terms of correlation with time to death (mortality risk).

[0009] Thus, data corresponding to any combination of at least four parameters selected from parameters (i) to (xxvi) or (i) to (xxix) below are suitable to form a useful score. For example, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty one, at least twenty two, at least twenty three, at least twenty four, at least twenty five, at least twenty six, at least twenty seven, at least twenty eight or twenty nine parameters selected from parameters (i) to (xxvi) or (i) to (xxix) can be used.

[0010] In a preferred embodiment, data corresponding to all thirteen parameters (i) to (xiii) below are selected.

[0011] A first aspect of the application provides a method of assessing the risk of death of a cancer patient, the method comprising inputting cancer patient information into a model to generate a score indicative of the risk of death of the cancer patient. The patient information can comprise data corresponding to each of the following parameters:

[0012] (i) albumin level in serum or plasma;

[0013] (ii) Eastern Cooperative Oncology Group (ECOG) performance status;

[0014] (iii) ratio of lymphocytes to white blood cells in blood;

[0015] (iv) smoking status;

[0016] (v) age;

[0017] (vi) TNM classification of malignant tumor stage;

[0018] (vii) heart rate;

[0019] (viii) chloride or sodium level in serum or plasma, preferably chloride level in serum or plasma;

[0020] (ix) urea nitrogen level in serum or plasma;

[0021] (x) gender;

[0022] (xi) haemoglobin or hematocrit level in blood, preferably haemoglobin level in blood;

[0023] (xii) aspartate aminotransferase enzyme activity level in serum or plasma; and

[0024] (xiii) alanine aminotransferase enzyme activity level in serum or plasma.

[0025] The present inventors have further shown that the use of 26 parameters or 29 parameters, or a subset thereof, is suitable for predicting the treatment response of a cancer patient to an anti-cancer therapy.

[0026] Accordingly, a second aspect of the present application provides a method of predicting the treatment response of a cancer patient to an anti-cancer therapy, the method comprising inputting cancer patient information into a model to generate a score indicative of the treatment response of the cancer patient. The patient information can comprise data corresponding to each of the following parameters:

[0027] (i) albumin level in serum or plasma;

[0028] (ii) Eastern Cooperative Oncology Group (ECOG) performance status;

[0029] (iii) ratio of lymphocytes to white blood cells in blood;

[0030] (iv) smoking status;

[0031] (v) age;

[0032] (vi) TNM classification of malignant tumor stage;

[0033] (vii) heart rate;

[0034] (viii) chloride or sodium level in serum or plasma, preferably chloride level in serum or plasma;

[0035] (ix) urea nitrogen level in serum or plasma;

[0036] (x) gender;

[0037] (xi) hemoglobin or hematocrit level in blood, preferably hemoglobin level in blood;

[0038] (xii) aspartate aminotransferase enzyme activity level in serum or plasma; and

[0039] (xiii) alanine aminotransferase enzyme activity level in serum or plasma.

[0040] The method can further comprise selecting a patient predicted to benefit from treatment with an anti-cancer therapy for treatment with an anti-cancer therapy, or treating a patient predicted to benefit from treatment with an anti-cancer therapy with an anti-cancer therapy.

[0041] There is also provided a method of treating a cancer patient with an anti-cancer therapy, the method comprising:

[0042] (i) predicting the treatment response of the cancer patient to the anti-cancer therapy; or

[0043] (ii) ranking test results of a method of predicting a treatment response of a cancer patient to an anti-cancer therapy;

[0044] wherein the method comprises inputting cancer patient information into a model to generate a score indicative of a treatment response of the cancer patient. The patient information can comprise data corresponding to each of the following parameters:

[0045] (i) albumin level in serum or plasma;

[0046] (ii) Eastern Cooperative Oncology Group (ECOG) performance status;

[0047] (iii) ratio of lymphocytes to white blood cells in blood;

[0048] (iv) smoking status;

[0049] (v) age;

[0050] (vi) TNM classification of malignant tumor stage;

[0051] (vii) heart rate;

[0052] (viii) chloride or sodium level in serum or plasma, preferably chloride level in serum or plasma;

[0053] (ix) urea nitrogen level in serum or plasma;

[0054] (x) gender;

[0055] (xi) hemoglobin or hematocrit level in blood, preferably hemoglobin level in blood;

[0056] (xii) aspartate aminotransferase enzyme activity level in serum or plasma; and

[0057] (xiii) alanine aminotransferase enzyme activity level in serum or plasma; and

[0058] administering a pharmaceutically effective amount of the anti-cancer therapy to a patient predicted to respond to the anti-cancer therapy.

[0059] Further provided is an anti-cancer therapy for use in a method of treating cancer in a patient, the method comprising predicting a treatment response of a cancer patient to an anti-cancer therapy, the method comprising inputting cancer patient information into a model to generate a score indicative of a treatment response of the cancer patient. The patient information can comprise data corresponding to each of the following parameters:

[0060] (i) albumin level in serum or plasma;

[0061] (ii) Eastern Cooperative Oncology Group (ECOG) performance status;

[0062] (iii) the ratio of lymphocytes to white blood cells in the blood;

[0063] (iv) smoking status;

[0064] (v) age;

[0065] (vi) TNM classification of the stage of the malignant tumour;

[0066] (vii) heart rate;

[0067] (viii) chloride or sodium levels in serum or plasma, preferably chloride levels in serum or plasma;

[0068] (ix) urea nitrogen levels in serum or plasma;

[0069] (x) gender;

[0070] (xi) haemoglobin or haematocrit levels in blood, preferably haemoglobin levels in blood;

[0071] (xii) aspartate aminotransferase enzyme activity levels in serum or plasma; and

[0072] (xiii) alanine aminotransferase enzyme activity levels in serum or plasma; and

[0073] administering a pharmaceutically effective amount of an anti-cancer therapy to a patient predicted to respond to the anti-cancer therapy.

[0074] In some embodiments of the disclosure, the patient information can comprise or consist of data corresponding to more than four parameters selected from parameters (i) to (xiii), but less than all of parameters (i) to (xiii). For example, the patient information can comprise or consist of data corresponding to five, six, seven, eight, nine, ten, eleven or twelve parameters selected from parameters (i) to (xiii). Alternatively, the patient information can comprise or consist of data corresponding to all thirteen parameters selected from parameters (i) to (xiii). Preferably, the patient information comprises or consists of data corresponding to at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve or all thirteen parameters selected from parameters (i) to (xiii). More preferably, the patient information comprises or consists of data corresponding to at least five, at least six, at least seven, at least eight or at least nine parameters selected from parameters (i) to (xiii). For example, the patient information can comprise or consist of data corresponding to at least five parameters selected from parameters (i) to (xiii). Alternatively, the patient information can comprise or consist of data corresponding to at least six parameters selected from said parameters (i) to (xiii). As a further selection, the patient information can comprise or consist of data corresponding to at least seven parameters selected from said parameters (i) to (xiii). As a still further selection, the patient information can comprise or consist of data corresponding to at least eight parameters selected from said parameters (i) to (xiii). As another selection, the patient information can comprise or consist of data corresponding to at least nine parameters selected from said parameters (i) to (xiii).

[0075] For example, the patient information can comprise or consist of data corresponding to all of parameters (i) to (v). The inventors have found that selecting these parameters can improve the accuracy of the mortality risk assessment.

[0076] Alternatively, the patient information can comprise or consist of data corresponding to all of parameters (i) to (xi) and one or both of (xii) and (xiii).

[0077] A cancer patient's treatment response to an anti-cancer therapy can be a complete response, progression-free survival, partial response, or cancer progression. A complete response (complete remission) can refer to the absence of detectable disease (cancer) in a patient. Progression-free survival can refer to a patient surviving for a period of time during which the disease (cancer) does not worsen. A partial response can refer to a reduction in tumor size or a reduction in the spread of cancer in a patient. A complete response (also referred to as complete remission) can refer to the absence of detectable disease in a patient. Cancer progression can refer to the worsening of disease (cancer), for example, an increase in tumor size and / or an increase in the number of tumors in a patient. Methods of detecting a complete response, partial response, progression-free survival, and cancer progression of a cancer patient's response to an anti-cancer therapy are well known in the art.

[0078] Unless the context requires otherwise, an anti-cancer therapy referred to herein can be a known anti-cancer therapy for the cancer in question, for example, radiotherapy, chemotherapy, immunotherapy, hormonal therapy, and / or surgery. For example, the anti-cancer therapy can be a known anti-cancer therapy for advanced non-small cell lung cancer (NSCLC), bladder cancer, chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), hepatocellular carcinoma (HCC), metastatic breast cancer, metastatic colorectal cancer (CRC), metastatic renal cell carcinoma (RCC), multiple myeloma, ovarian cancer, small cell lung cancer (SCLC). The anti-cancer therapy can alternatively be a known anti-cancer therapy for follicular lymphoma, pancreatic cancer, or head and neck cancer.

[0079] A patient referred to herein is preferably a human patient. Where the method comprises predicting a treatment response to an anti-cancer therapy, the patient can be a patient who has not previously been treated with the anti-cancer therapy, unless the context requires otherwise.

[0080] The patient information can further comprise data corresponding to one or more parameters selected from:

[0081] (xiv) systolic or diastolic blood pressure, preferably systolic blood pressure;

[0082] (xv) lactate dehydrogenase enzyme activity level in serum or plasma;

[0083] (xvi) body mass index;

[0084] (xvii) protein level in serum or plasma;

[0085] (xviii) platelet level in blood;

[0086] (xix) number of metastatic sites;

[0087] (xx) ratio of eosinophils to leukocytes in blood;

[0088] (xxi) serum or plasma calcium levels;

[0089] (xxii) oxygen saturation level in arterial blood;

[0090] (xxiii) alkaline phosphatase enzyme activity level in serum or plasma;

[0091] (xxiv) neutrophil-to-lymphocyte ratio (NLR) in blood;

[0092] (xxv) total bilirubin levels in serum or plasma; and

[0093] (xxvi) White blood cell levels in the blood.

[0094] Additionally or alternatively, the patient information may further include data corresponding to one or more parameters selected from the group consisting of:

[0095] (xxvii) lymphocyte levels in the blood;

[0096] (xxviii) carbon dioxide levels in the blood; and

[0097] (xxix) Monocyte levels in blood.

[0098] In some embodiments, the patient information may include data corresponding to more than one parameter selected from parameters (xiv) to (xxvi) and / or (xxvii) to (xxix). For example, the patient information may additionally or alternatively include data corresponding to at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or all thirteen parameters selected from parameters (xiv) to (xxvi) and / or at least one, at least two, or all three parameters selected from parameters (xxvii) to (xxix). In some embodiments, parameters other than (i) to (xxvi) or (i) to (xxix) may also be included in the patient information and training data.

[0099] For example, the patient information may comprise or consist of data corresponding to at least four parameters selected from parameters (i) to (xiii), and at least two further parameters selected from parameters (i) to (xxvi) or (i) to (xxix).

[0100] Alternatively, the patient information may include data corresponding to at least seven parameters selected from parameters (i) to (xxix), wherein at least one parameter is selected from parameters (xxvii) to (xxix).

[0101] For example, the cancer patient information can comprise data corresponding to at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, at least twenty-seven, or at least twenty-eight parameters selected from the group consisting of parameters (i) to (xxix), wherein at least one parameter is selected from the group consisting of parameters (xxvii) to (xxix).

[0102] The parameter is preferably or comprises parameter (xxvii) (lymphocyte levels in blood).

[0103] As a further selection, the patient information can comprise data corresponding to parameter (xxvii) and five or more parameters selected from the group consisting of parameters (i), (ii), (iii), (v), (vi), (vii), (viii), (ix), (xi), (xviii) and (xxiii).

[0104] For example, the cancer patient information can comprise data corresponding to parameter (xxvii) and at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, at least twenty-seven, at least twenty-eight, or all twenty-nine parameters selected from the group consisting of parameters (i) to (xxix), wherein at least one parameter is selected from the group consisting of parameters (xxvii) to (xxix).

[0105] The patient information can comprise or consist of data corresponding to all of parameters (i) to (xxvi). The inventors have shown that all of these parameters make an independent contribution to the model. Such a score shows a good correlation with time to death (r 2 = 0.24) on an example dataset and is significantly better than RMHS (r 2 = 0.02) on the same example dataset. Preferably, the patient information comprises or consists of data corresponding to all of parameters (i) to (xxix). The inventors have shown that all of these parameters make an independent contribution to the model. Such a score shows an improved correlation with time to death (r 2 = 0.30) on an example dataset and is again significantly better than RMHS (r 2 = 0.04) on the same example dataset.

[0106] Generally, using more parameters (i) to (xiii), (i) to (xxvi), or (i) to (xxix) results in more accurate models and scores, but it can be difficult in practice to obtain patient information and / or training data for one or more parameters, so the accuracy of the models and scores can be balanced against practical constraints.

[0107] The models can be formed by performing a statistical significance analysis on training data, the training data including the parameters selected for a plurality of subjects. The statistical significance analysis can include a multivariate cox regression analysis. In some embodiments, one or more other statistical analysis techniques can be used.

[0108] The training data can also include information indicative of the risk of death of the subjects. For example, the training data can include an indication of overall survival, time to event, time between first line treatment and last recorded contact, or other indication of the risk of death for each subject. The training data can include censored follow-up times indicating the time elapsed from first line treatment to the date of the patient’s last contact with their clinic. The last contact can be the last visit, drug administration, sample collection, or other contact.

[0109] It is advantageous to select to use parameters (i) to (xxvi) or (i) to (xxix) because these parameters are routinely measured and / or available in the clinic. Thus, the score is easy to use because data for parameters (i) to (xxvi) or (i) to (xxix) is available for most cancer patients, while still providing a highly accurate prediction.

[0110] Useful scores can be obtained even if not all parameters are available for a patient. Thus, the number of parameters selected for patient information can be less than the number of parameters included in the training data. For example, if a model is formed using training data including all parameters (i) to (xxvi) or (i) to (xxix), the patient information can include less than all of these parameters, and still a score with similar accuracy can be produced. For example, 13, 14, 15, or 16 parameters from the list (i) to (xxvi) or (i) to (xxix) can be input into a model including all parameters (i) to (xxvi) or (i) to (xxix). This increases the ease of use of the method because the method can still evaluate patients for which patient information is missing.

[0111] Training data can be obtained from a database, which can be from electronic health record data. For example, the Flatiron Health database. The database and / or training data can include structured and / or unstructured data. Subjects in the training data can be cancer patients. Subjects can be included or excluded from the training data based on cancer type. For example, to tailor the method for patients with a first cancer type, the training data can exclude subjects who do not have the first cancer type.

[0112] Parameters can be selected for use in the model based on availability in the training data and / or patient data. For example, in some embodiments, only parameters that are available for at least 75% of patients in the database can be selected for the model, or only parameters that are available for more than 25% of patients in the database can be selected for the model. To improve the training data, patients who lack treatment information can be excluded from the training data. Missing data can be imputed to improve the training data. This can be performed using a suitable algorithm, for example, the missForest R package.

[0113] To improve the training data, peripheral data can be excluded. For example, for continuous parameters, observations that differ from the mean by more than 4 standard deviations can be excluded.

[0114] Data can be screened so that unimportant parameters are excluded from the model. The inventors have found that parameters (i) to (xxvi) and (i) to (xxix) are important in the assessment of risk of death and prediction of treatment response to anticancer therapy, with models employing parameters (i) to (xxix) outperforming models employing parameters (i) to (xxvi). Screening can include analyzing each parameter using a Bonferroni correction and excluding parameters with a p-value greater than a threshold value. For example, parameters with a p-value of 0.05 divided by the number of parameters considered or greater can be excluded from the training data. Parameters can be included in the model in order of their importance (predictive value) during screening.

[0115] The predictive values of parameters (i) to (xxvi) in RoPro 1 are detailed in Table 15, where a rank of (1) indicates the most predictive parameter (albumin) and a rank of (26) indicates the least predictive parameter (white blood cell level) among parameters (i) to (xxvi) in that model. The predictive values of parameters (i) to (xxix) in RoPro 2 are also detailed in Table 15, where a rank of (1) again indicates the most predictive parameter (albumin) and a rank of (29) indicates the least predictive parameter (carbon dioxide level) among parameters (i) to (xxix) in that model. Thus, the lower the rank number of a parameter in Table 15, the more preferred it is for a patient’s information to include data corresponding to that parameter.

[0116] As the inventors have found that parameters (i) to (xxvi) are important in the ordering of the parameters listed above in (i) to (xxvi) (corresponding to the ordering shown in Table 15) in RoPro1, a model can be formed by including parameters selected from parameters (i) to (xxvi) in that order.

[0117] In a preferred embodiment, the patient information can thus comprise or consist of data corresponding to parameters having a rank of 1 to 4, 1 to 5, 1 to 6, 1 to 7, 1 to 8, 1 to 9, 1 to 10, 1 to 11, 1 to 12, 1 to 13, 1 to 14, 1 to 15, 1 to 16, 1 to 17, 1 to 18, 1 to 19, 1 to 20, 1 to 21, 1 to 22, 1 to 23, 1 to 24, 1 to 25 or 1 to 26 in RoPro1 as shown in Table 15, wherein the parameters having a rank of 1 to 4 in RoPro1 correspond to albumin level in serum or plasma, ECOG performance status, ratio of lymphocytes to white blood cells in blood and smoking status.

[0118] As the inventors have found that parameters (i) to (xxix) are important in the ordering of RoPro2 shown in Table 15, and that such a model more accurately predicts OS than a model based on parameters (i) to (xxvi), in one preferred embodiment a model can be formed by including parameters selected from parameters (i) to (xxix) in the ordering shown in Table 15.

[0119] Thus, in a more preferred embodiment, the patient information can comprise or consist of data corresponding to parameters having a rank of 1 to 4, 1 to 5, 1 to 6, 1 to 7, 1 to 8, 1 to 9, 1 to 10, 1 to 11, 1 to 12, 1 to 13, 1 to 14, 1 to 15, 1 to 16, 1 to 17, 1 to 18, 1 to 19, 1 to 20, 1 to 21, 1 to 22, 1 to 23, 1 to 24, 1 to 25, 1 to 26, 1 to 27, 1 to 28 or 1 to 29 in RoPro2 as shown in Table 15, wherein the parameters having a rank of 1 to 4 in RoPro2 correspond to albumin level in serum or plasma, lymphocyte level in blood, ECOG performance status, white blood cell level in blood.

[0120] Thus, the present disclosure provides:

[0121] [1] A method or anticancer therapy for use in a method as described herein, wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more parameters.

[0122] [2] The method or anticancer therapy for use in a method according to [1], wherein the parameters include albumin level in serum or plasma of the patient.

[0123] [3] The method or anticancer therapy for use in a method according to [1] or [2], wherein the parameter comprises the ECOG performance status of the patient.

[0124] [4] The method or anticancer therapy for use in a method according to any one of [1] to [3], wherein the parameter comprises the ratio of lymphocytes to leukocytes in the blood of the patient.

[0125] [5] The method or anticancer therapy for use in a method according to any one of [1] to [4], wherein the parameter comprises the smoking status of the patient.

[0126] [6] The method or anticancer therapy for use in a method according to any one of [1] to [5], wherein the parameter comprises the age of the patient.

[0127] [7] The method or anticancer therapy for use in a method according to any one of [1] to [6], wherein the parameter comprises the TNM classification of the malignant tumor stage of the patient.

[0128] [8] The method or anticancer therapy for use in a method according to any one of [1] to [7], wherein the parameter comprises the heart rate of the patient.

[0129] [9] The method or anticancer therapy for use in a method according to any one of [1] to [8], wherein the parameter comprises the chloride or sodium level in the serum or plasma of the patient, preferably the chloride level in the serum or plasma of the patient.

[0130]

[10] The method or anticancer therapy for use in a method according to any one of [1] to [9], wherein the parameter comprises the urea nitrogen level in the serum or plasma of the patient.

[0131]

[11] The method or anticancer therapy for use in a method according to any one of [1] to

[10] , wherein the parameter comprises the gender of the patient.

[0132]

[12] The method or anticancer therapy for use in a method according to any one of [1] to

[11] , wherein the parameter comprises the hemoglobin or hematocrit level in the blood of the patient, preferably the hemoglobin level in the blood.

[0133]

[13] The method or anticancer therapy for use in a method according to any one of [1] to

[12] , wherein the parameter comprises the aspartate aminotransferase enzyme activity level in the serum or plasma of the patient.

[0134]

[14] The method or anticancer therapy for use in a method according to any one of [1] to

[13] , wherein the parameter comprises the level of alanine aminotransferase enzyme activity in the patient’s serum or plasma.

[0135]

[15] The method or anticancer therapy for use in a method according to any one of [1] to

[14] , wherein the parameter comprises the systolic or diastolic blood pressure, preferably the systolic blood pressure, of the patient.

[0136]

[16] The method or anticancer therapy for use in a method according to any one of [1] to

[15] , wherein the parameter comprises the level of lactate dehydrogenase enzyme activity in the patient’s serum or plasma.

[0137]

[17] The method or anticancer therapy for use in a method according to any one of [1] to

[16] , wherein the parameter comprises the body mass index of the patient.

[0138]

[18] The method or anticancer therapy for use in a method according to any one of [1] to

[17] , wherein the parameter comprises the level of protein in the patient’s serum or plasma.

[0139]

[19] The method or anticancer therapy for use in a method according to any one of [1] to

[18] , wherein the parameter comprises the level of platelets in the patient’s blood.

[0140]

[20] The method or anticancer therapy for use in a method according to any one of [1] to

[19] , wherein the parameter comprises the number of metastatic sites in the patient’s body.

[0141]

[21] The method or anticancer therapy for use in a method according to any one of [1] to

[20] , wherein the parameter comprises the ratio of eosinophils to leukocytes in the patient’s blood.

[0142]

[22] The method or anticancer therapy for use in a method according to any one of [1] to

[21] , wherein the parameter comprises the level of calcium in the patient’s serum or plasma.

[0143]

[23] The method or anticancer therapy for use in a method according to any one of [1] to

[22] , wherein the parameter comprises the level of oxygen saturation in the patient’s arterial blood.

[0144]

[24] The method or anticancer therapy for use in a method according to any one of [1] to

[23] , wherein the parameter comprises the level of alkaline phosphatase enzyme activity in the patient’s serum or plasma.

[0145]

[25] The method or anticancer therapy for use in a method according to any one of [1] to

[24] , wherein the parameter comprises the NLR in the patient’s blood.

[0146]

[26] The method or anticancer therapy for use in a method of any one of [1] to

[25] , wherein the parameter comprises a total bilirubin level in serum or plasma of the patient.

[0147]

[27] The method or anticancer therapy for use in a method of any one of [1] to

[26] , wherein the parameter comprises a white blood cell level in blood of the patient.

[0148]

[28] The method or anticancer therapy for use in a method of any one of [1] to

[27] , wherein the parameter comprises a lymphocyte level in blood of the patient.

[0149]

[29] The method or anticancer therapy for use in a method of any one of [1] to

[28] , wherein the parameter comprises a carbon dioxide level in blood of the patient.

[0150]

[30] The method or anticancer therapy for use in a method of any one of [1] to

[29] , wherein the parameter comprises a monocyte level in blood of the patient.

[0151]

[31] The method or anticancer therapy for use in a method of any one of [1] to

[30] , wherein the patient information comprises data corresponding to four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, or twenty-nine parameters selected from the parameters listed in [2] to

[30] .

[0152] One or more of the parameters listed in (i) to (xxix) above can be replaced with a suitable surrogate parameter related to the relevant parameter. Suitable surrogate parameters are listed in Table 15. For example, the chloride level in serum or plasma can be replaced with the sodium level in serum or plasma, the hemoglobin level in blood can be replaced with the hematocrit level in blood, the alanine aminotransferase (ALT) enzyme activity level in serum or plasma can be replaced with the aspartate aminotransferase (AST) enzyme activity level in serum or plasma, and / or the systolic blood pressure can be replaced with the diastolic blood pressure.

[0153] Methods for measuring or assessing the above parameters (i) to (xxix), and alternative parameters mentioned herein, are known in the art and routinely measured in a clinical setting. It is therefore well within the capabilities of the skilled person to measure these parameters. The method of measurement of a given parameter is preferably consistent between the cancer patients being analysed and any training or validation datasets. Where necessary, results from different methods of measurement can be normalised to allow comparison between data obtained from said methods.

[0154] Exemplary methods for measuring the above parameters (i) to (xxix), and alternative parameters mentioned herein, are detailed in Table 15. Where available, Table 15 also lists the LOINC code (version 2.65; published 14 December 2018) for the parameters mentioned herein. Information for a given parameter, including the applicable method of measurement, stored under its LOINC code can be retrieved from: https: / / search.loinc.org / searchLOINC / .

[0155] TNM staging of a tumour can be determined according to the 8th Edition UICC TNM Classification of Malignant Tumours from https: / / www.uicc.org / 8th-edition-uicc-tnm-classification-malignant-tumors-published [accessed 27 March 2019]. Measurement of the parameters mentioned herein can be in any suitable unit of measurement, for example those listed in Table 15 for these parameters. The unit of measurement of a given parameter is preferably consistent between the cancer patients being analysed and any training or validation datasets. Where necessary, different units of measurement can be converted to a common unit of measurement.

[0156] The above parameters (i) to (xxix), and alternative parameters mentioned herein, can be measured at any suitable time point.

[0157] For example, in the context of a method comprising assessing the risk of death of a cancer patient, for example a method of selecting a cancer patient for inclusion in a clinical trial or a method of treatment with an anti-cancer therapy, the parameters can be measured prior to the start of the clinical trial or the administration of the first dose of anti-cancer therapy to the patient, respectively. The measurement is made shortly before the start of the clinical trial or the administration of the first dose of anti-cancer therapy to the patient, for example 6 months or less, 3 months or less, or 1 month or less.

[0158] When the method is a method for predicting a therapeutic response of a cancer patient to an anticancer therapy, the parameter can be measured before the first dose of the anticancer therapy is administered to the patient. Alternatively, the parameter can be measured after the anticancer therapy is administered to the patient to predict the therapeutic response to the anticancer therapy during treatment. In one embodiment, the parameter can be measured at a first time point and a second time point, wherein the first time point can be before the first dose of the anticancer therapy is administered to the patient, and the second time point can be after the anticancer therapy is administered to the patient, wherein an improvement in the predicted therapeutic response at the second time point compared to the first time point indicates that the patient has responded to the anticancer therapy, and a worsening of the predicted therapeutic response at the second time point compared to the first time point indicates that the patient has not responded to the anticancer therapy or has developed resistance to the anticancer therapy.

[0159] Where the method comprises forming a model by performing a multivariate Cox regression analysis on training data, the training data may include patient information comprising parameter data for a plurality of subjects. The plurality of subjects may include at least 10,000 subjects. Using a large number of subjects may improve the accuracy of the model. For example, the plurality of subjects may include at least 15,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, or 90,000 subjects.

[0160] Forming the model may comprise assigning a respective mean m to each respective parameter selected from a list of the plurality of subjects, and assigning a respective weight w to each respective parameter selected from the list, and wherein an output of the model is given by the sum of the selected parameters according to the formula: output = Σw(input - m). The score may thus be centered at 0.

[0161] A method of assessing the risk of death for a cancer patient according to any disclosed embodiment may include comparing a score generated by a model to one or more predetermined thresholds, or comparing the generated score to scores generated for other cancer patients in the same group, to assess the risk of death. For example, the method may include determining whether the generated score is above or below a predetermined threshold, or within a range of values ​​between two different predetermined thresholds.

[0162] A method for predicting a cancer patient's response to an anti-cancer therapy according to any disclosed embodiment may include comparing a score generated by a model to one or more predetermined thresholds, or comparing the generated score to scores generated for other cancer patients in the same group, to obtain a prediction of treatment response. For example, the method may include determining whether the generated score is above or below a predetermined threshold, or within a range of values ​​between two different predetermined thresholds.

[0163] The risk of death can be assessed as high risk or low risk. For example, a patient can be assessed as being at high risk of death if their score is higher than 0, or higher than 1, or higher than 1.05. A patient can be assessed as being at low risk of death if their score is lower than 0, or lower than -1, or lower than -1.19. The risk of death can be assessed as very high risk if the RoPro score is higher than 1.13. The risk of death can be assessed as lower if the RoPro score is lower than 1.13. For the advanced NSCLC specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.81. For the advanced melanoma specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.06. For the bladder cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.99. For the CLL specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.16. For the DLBCL specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.17. For the HCC specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.11. For the metastatic breast cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.00. For the metastatic CRC specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.94. For the RCC specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.22. For the multiple myeloma specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.02. For the ovarian cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.04. For the SCLC specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.89. For the head and neck cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.75. For the follicular cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 1.60. For the pancreatic cancer specific RoPro score, the risk of death can be assessed as very high if the RoPro score is higher than 0.87.

[0164] Alternatively, the patient can be one of a group of patients, and a score can be generated for each patient in the group. The patient can then be assessed as being at high mortality risk if their score is in the top 50% or top 10% or top 5% of scores in the group. The patient can be assessed as being at low mortality risk if their score is in the bottom 50% or bottom 10% or bottom 5% of scores in the group. Based on the comparison of the patient’s score to the scores of the subjects in the training data, the patient’s mortality risk can be assessed as high or low. Scores can be generated for a plurality of subjects in the training data, and the patient’s score can be compared to the distribution of scores in the training data. The patient can be assessed as being at high mortality risk if their score is in the top 50% or top 10% or top 5% of scores of the plurality of subjects. The patient can be assessed as being at low mortality risk if their score is in the bottom 50% or bottom 10% or bottom 5% of scores of the plurality of subjects.

[0165] As used herein, the term ratio can refer to a scaled ratio. For example, the ratio of lymphocytes to white blood cells in the blood can be the number of lymphocytes per 100 white blood cells. Similarly, the ratio of eosinophils to white blood cells in the blood can be the number of eosinophils per 100 white blood cells.

[0166] The method of assessing whether a cancer patient is at high mortality risk or low mortality risk can be used in a number of different situations.

[0167] For example, when conducting a clinical trial, for example for an anti-cancer therapy, it is advantageous to only select patients who are likely to survive throughout the trial for inclusion in the clinical trial. This is beneficial from the perspective of conducting the clinical trial, as data from patients who drop out of the trial due to death cannot be used in many cases to assess the anti-cancer therapy for safety or efficacy, thereby increasing the cost of the clinical trial and the time required to complete the trial. Furthermore, including patients with a high mortality risk in a clinical trial can mask the effect of the treatment on patients with a low mortality risk or lower mortality risk, as the high mortality risk patients are too unwell to benefit from the treatment. Furthermore, excluding patients with a high mortality rate from a clinical trial is beneficial to the patients, as individuals who are unlikely to benefit from the anti-cancer therapy being tested are not exposed to unnecessary treatment and any accompanying side effects.

[0168] Accordingly, in one embodiment, the present application provides a method of selecting a cancer patient for inclusion in a clinical trial, for example for an anti-cancer therapy, the method comprising assessing whether the cancer patient is at high mortality risk or low mortality risk using a method as described herein, and selecting a patient assessed as being at low mortality risk for inclusion in the clinical trial.

[0169] For the evaluation of the results of a clinical trial, it is important that the patients in the group receiving the anti-cancer therapy under test and the patients in the control group receiving placebo or no treatment as a treatment group are well matched, i.e. have the same risk of death as assessed using the methods described herein, to ensure that any effect seen in the treatment group is a result of the anti-cancer therapy under test and not caused by differences between the groups of patients. For example, if the risk of death in the placebo group is significantly higher than in the treatment group, this can falsely suggest that the treatment has a positive effect on patient survival, and vice versa.

[0170] In another embodiment, the present application thus provides a method of evaluating the results of a clinical trial on cancer patients, e.g. for an anti-cancer therapy, the method comprising using the methods described herein to assess whether a cancer patient participating in the clinical trial is at high risk of death or at low risk of death.

[0171] In a further embodiment, the present application relates to a method of selecting a cancer patient for inclusion in a clinical trial, e.g. for an anti-cancer therapy, the method comprising using the methods described herein to determine a first cancer patient and a second cancer patient having the same risk of death, and including the patients in the clinical trial. The first cancer patient can receive the anti-cancer therapy, while the second cancer patient can not receive the anti-cancer therapy. In this case, the second cancer patient acts as a control for the first cancer patient, allowing e.g. the safety or efficacy of the anti-cancer therapy under evaluation. Preferably, both the first and second patients have a low risk of death.

[0172] If patients are both assessed as having a low risk or both assessed as having a high risk, it can be judged that the patients have the same risk of death. Alternatively, if the scores of the patients are within the same decile of each other, it can be judged that the patients have the same risk. For example, a group of patients can be divided into deciles of 10% or 5% according to their scores. Two patients in the first 5% of scores in the group can be judged to have the same risk. Two patients in the second (5-10%) decile can be judged to have the same risk.

[0173] The present application also provides a method of comparing a first cancer patient or group of cancer patients to a second cancer patient or group of cancer patients, respectively, the method comprising using the methods as described herein to assess whether the patient or patients in the first and second groups are at high risk of death or at low risk of death.

[0174] As described above, patients at high risk of death are less likely to benefit from an anti-cancer therapy. It is advantageous to determine such patients prior to treatment, as it avoids exposing patients to therapies that ultimately prove ineffective and any side effects associated therewith. Given that many anti-cancer therapies are also associated with high costs, the ability to determine such patients prior to treatment also reduces the cost burden on the healthcare system.

[0175] In another embodiment, the present application thus relates to a method of selecting a cancer patient for treatment with an anti-cancer therapy, the method comprising assessing whether the cancer patient is at high risk of death or at low risk of death using the methods described herein, and selecting a cancer patient assessed to be at low risk of death for treatment with an anti-cancer therapy. The method can further comprise treating a cancer patient assessed to be at low risk of death with an anti-cancer therapy.

[0176] Also provided is a method of treating a cancer patient with an anti-cancer therapy, the method comprising assessing whether the cancer patient is at high risk of death or at low risk of death using the methods as described herein, and administering a pharmaceutically effective amount of an anti-cancer therapy to a patient assessed to be at low risk of death.

[0177] Further provided is an anti-cancer therapy for use in a method of treating a cancer patient with an anti-cancer therapy, the method comprising assessing whether the cancer patient is at high risk of death or at low risk of death using the methods as described herein, and administering a pharmaceutically effective amount of an anti-cancer therapy to a patient assessed to be at low risk of death.

[0178] Similarly, the risk of death of a patient during cancer treatment is expected to indicate whether the patient is benefiting or will benefit from the therapy.

[0179] Thus, also provided is a method of monitoring a cancer patient during treatment with an anti-cancer therapy, wherein the patient can optionally show disease progression, the method comprising assessing whether the cancer patient is at high risk of death or at low risk of death using the methods described herein, wherein a cancer patient assessed to be at low risk of death is selected to continue treatment with an anti-cancer therapy, and a cancer patient assessed to be at high risk of death is selected to stop treatment with an anti-cancer therapy.

[0180] Parameters for predicting prognosis of a particular cancer type are known in the art, as are methods of measuring or assessing these parameters. The present inventors have shown that when assessing, for example, the risk of death in a cancer patient, or predicting the treatment response of a cancer patient to an anti-cancer therapy, the further incorporation of one or more cancer type specific parameters (e.g. for predicting prognosis of advanced non-small cell lung cancer (NSCLC), bladder cancer, chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), hepatocellular carcinoma (HCC), metastatic breast cancer, metastatic colorectal cancer (CRC), metastatic renal cell carcinoma (RCC), multiple myeloma, ovarian cancer, small cell lung cancer (SCLC), head and neck cancer, or pancreatic cancer) into the patient information results in a more accurate risk of death score compared to using parameters (i) to (xxvi) or (i) to (xxix) alone. Thus, the patient information in a method of assessing the risk of death in a cancer patient can further comprise data corresponding to one or more parameters selected from parameters known to be indicative of prognosis of advanced NSCLC, bladder cancer, CLL, DLBCL, HCC, metastatic breast cancer, metastatic CRC, metastatic RCC, multiple myeloma, ovarian cancer, SCLC, head and neck cancer, and pancreatic cancer, such as those listed in Table 15. These cancer specific parameters can be measured at the same time point as the other parameters employed in the method, or at a different time point.

[0181] Thus, the present application further provides:

[0182]

[32] A method or anti-cancer therapy for use in a method as described herein, wherein the cancer patient is a patient with advanced NSCLC, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more NSCLC specific parameters.

[0183]

[33] The method or anti-cancer therapy for use in a method according to

[32] , wherein the NSCLC specific parameter is the presence or absence of squamous cell carcinoma in the patient.

[0184]

[34] The method or anti-cancer therapy for use in a method according to

[32] or

[33] , wherein the NSCLC specific parameter is the positive or negative PD-L1 expression status of the patient’s primary tumor.

[0185]

[35] The method or anti-cancer therapy for use in a method according to any one of

[32] to

[34] , wherein the NSCLC specific parameter is the presence or absence of ALK rearrangement in the patient’s tumor.

[0186]

[36] The method or anticancer therapy for use in a method according to any one of

[32] to

[35] , wherein the NSCLC-specific parameter is the presence or absence of an EGFR mutation in the patient’s tumor.

[0187]

[37] The method or anticancer therapy for use in a method according to any one of

[32] to

[36] , wherein the NSCLC-specific parameter is the presence or absence of a KRAS mutation in the patient’s tumor.

[0188]

[38] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a bladder cancer patient, and wherein the method comprises inputting cancer patient information into the model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more bladder cancer-specific parameters.

[0189]

[39] The method or anticancer therapy for use in a method according to

[38] , wherein the bladder cancer-specific parameter is the patient’s presence or absence of a history of cystectomy.

[0190]

[40] The method or anticancer therapy for use in a method according to

[38] or

[39] , wherein the bladder cancer-specific parameter is the patient’s N stage of the tumor at initial diagnosis.

[0191]

[41] The method or anticancer therapy for use in a method according to any one of

[38] to

[40] , wherein the bladder cancer-specific parameter is the patient’s T stage of the tumor at initial diagnosis.

[0192]

[42] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a CLL patient, and wherein the method comprises inputting cancer patient information into the model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more CLL-specific parameters.

[0193]

[43] The method or anticancer therapy for use in a method according to

[42] , wherein the CLL-specific parameter is the patient’s percent hematocrit per volume of blood.

[0194]

[44] The method or anticancer therapy for use in a method according to

[42] or

[43] , wherein the CLL-specific parameter is the ratio of monocytes to white blood cells in the blood, preferably the ratio of monocytes to 100 white blood cells in the patient’s blood.

[0195]

[45] The method or anticancer therapy for use in a method according to any one of

[42] to

[44] , wherein the CLL-specific parameter is the presence or absence of a 17p deletion in the patient’s tumor.

[0196]

[46] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a DLBCL patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and the positive or negative expression status of CD5 in the patient's bone marrow.

[0197]

[47] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a HCC patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and whether the patient has ascites, preferably data on whether the patient has or does not have ascites 60 days prior to or within 60 days of administration of the anticancer therapy (e.g. systemic anticancer therapy) to the patient.

[0198]

[48] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a metastatic breast cancer patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more metastatic breast cancer specific parameters.

[0199]

[49] The method or anticancer therapy for use in a method according to

[48] , wherein the metastatic breast cancer specific parameter is the positive or negative ER status of the patient's tumor.

[0200]

[50] The method or anticancer therapy for use in a method according to

[48] or

[49] , wherein the metastatic breast cancer specific parameter is the positive or negative PR status of the patient's tumor.

[0201]

[51] The method or anticancer therapy for use in a method according to any one of

[48] to

[50] , wherein the metastatic breast cancer specific parameter is the positive or negative HER2 status of the patient's tumor.

[0202]

[52] The method or anticancer therapy for use in a method according to any one of

[48] to

[51] , wherein the metastatic breast cancer specific parameter is the ratio of granulocytes to white blood cells in the patient's blood, preferably the ratio of granulocytes to 100 white blood cells in the patient's blood.

[0203]

[53] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a metastatic CRC patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more metastatic CRC-specific parameters.

[0204]

[54] The method or anticancer therapy for use in a method according to

[53] , wherein the metastatic CRC-specific parameter is the presence or absence of a BRAF mutation in the patient’s tumor.

[0205]

[55] The method or anticancer therapy for use in a method according to

[53] or

[54] , the metastatic CRC-specific parameter is the presence or absence of a KRAS mutation or rearrangement in the patient’s tumor.

[0206]

[56] The method or anticancer therapy for use in a method according to any one of

[53] to

[55] , wherein the metastatic CRC-specific parameter is the presence or absence of microsatellite instability (MSI-H) in the patient’s primary tumor, and loss of MMR protein expression or normal MMR protein expression.

[0207]

[57] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a metastatic RCC patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more metastatic RCC-specific parameters.

[0208]

[58] The method or anticancer therapy for use in a method according to

[57] , wherein the metastatic RCC-specific parameter is the presence or absence of a history of nephrectomy in the patient.

[0209]

[59] The method or anticancer therapy for use in a method according to

[57] or

[58] , wherein the metastatic RCC-specific parameter is the presence or absence of clear cell RCC in the patient, or the presence or absence of predominantly clear cell RCC in the patient, wherein the presence or absence of clear cell RCC in the patient is optionally determined by histology.

[0210]

[60] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a multiple myeloma patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising data corresponding to four or more of the parameters listed above in [2] to

[30] and one or more multiple myeloma-specific parameters.

[0211]

[61] The method or anticancer therapy for use in a method according to

[60] , wherein the multiple myeloma-specific parameter is the presence or absence of an abnormality in the patient’s tumor, wherein the presence or absence of the abnormality is optionally determined using FISH or karyotyping.

[0212]

[62] The method or anticancer therapy for use in a method according to

[60] or

[61] , wherein the multiple myeloma-specific parameter is the presence or absence of a myeloma (M) protein of immunoglobulin class IgA.

[0213]

[63] The method or anticancer therapy for use in a method according to any one of

[60] to

[62] , wherein the multiple myeloma-specific parameter is the presence or absence of a M protein of immunoglobulin class IgG.

[0214]

[64] The method or anticancer therapy for use in a method according to any one of

[60] to

[63] , the multiple myeloma-specific parameter is the presence or absence of a kappa light chain myeloma.

[0215]

[65] The method or anticancer therapy for use in a method according to any one of

[60] to

[64] , the multiple myeloma-specific parameter is the presence or absence of a lambda light chain myeloma.

[0216]

[66] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is an ovarian cancer patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising corresponding to four or more of the parameters listed above in [2] to

[30] and an ovarian cancer-specific parameter, wherein the ovarian cancer-specific parameter is the presence or absence of clear cell ovarian cancer.

[0217]

[67] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is an SCLC patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising corresponding to four or more of the parameters listed above in [2] to

[30] and an SCLC-specific parameter, wherein the SCLC-specific parameter is the presence or absence of extensive disease or limited disease at initial diagnosis.

[0218]

[68] A method or anticancer therapy for use in a method as described herein, wherein the cancer patient is a head and neck cancer patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising corresponding to four or more of the parameters listed above in [2] to

[30] and a head and neck cancer-specific parameter, wherein the head and neck cancer-specific parameter is human papillomavirus (HPV) status.

[0219]

[69] A method or anticancer therapy used in a method as described herein, wherein the cancer patient is a pancreatic cancer patient, and wherein the method comprises inputting cancer patient information into a model to generate a score, the patient information comprising parameters corresponding to four or more of the above-listed parameters of [2] to

[30] and a pancreatic cancer specific parameter, wherein the pancreatic cancer specific parameter is resection of a primary pancreatic tumor by surgery.

[0220] The cancer type specific parameters mentioned above are well known in the art, as are methods for measuring these parameters. Exemplary methods are listed in Table 15. Thus, measuring these parameters is well within the capabilities of the skilled person. For example, if the cancer type specific parameter is a biomarker (e.g. presence or absence of ALK rearrangement, presence or absence of EGFR mutation, etc.), information on the presence or absence of the biomarker is contained in the patient’s electronic health record (EHR). Methods for determining the presence or absence of a biomarker include sequencing, e.g. next generation sequencing, fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC).

[0221] The dataset from the Flatiron Health database on which the 26 parameters of RoPro1 described above were determined consisted of 99,249 patients with one of the following cancer types: advanced melanoma, advanced non-small cell lung cancer (NSCLC), bladder cancer, chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), hepatocellular carcinoma (HCC), metastatic breast cancer, metastatic colorectal cancer (CRC), metastatic renal cell carcinoma (RCC), multiple myeloma, ovarian cancer or small cell lung cancer (SCLC). Furthermore, the present inventors have shown that patient information comprising data corresponding to the parameters listed in (i) to (xxvi) above can be used to predict the risk of death for these cancer types. Thus, the cancer referred to herein can be a cancer selected from the group consisting of melanoma (e.g. advanced melanoma), NSCLC (e.g. advanced NSCLC), bladder cancer, CLL, DLBCL, HCC, metastatic breast cancer, metastatic CRC, metastatic RCC, multiple myeloma, ovarian cancer and SCLC.

[0222] The data set from the Flatiron Health database for which the 29 parameters of the above-mentioned RoPro 2 were determined consists of 111,538 patients with one of the following cancer types: advanced melanoma, advanced NSCLC, bladder cancer, CLL, DLBCL, HCC, metastatic breast cancer, metastatic CRC, metastatic RCC, multiple myeloma, ovarian cancer, SCLC, follicular lymphoma, pancreatic cancer or head and neck cancer. In addition, the inventors have shown that patient information containing data corresponding to the parameters listed in (i) to (xxix) above can be used to predict the risk of death from these cancer types. Therefore, the cancer mentioned herein can be a cancer selected from the group consisting of: melanoma (e.g., advanced melanoma), NSCLC (e.g., advanced NSCLC), bladder cancer, CLL, DLBCL, HCC, metastatic breast cancer, metastatic CRC, metastatic RCC, multiple myeloma, ovarian cancer, SCLC, follicular lymphoma, pancreatic cancer and head and neck cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0223] Figure 1A and 1C : Hazard ratio (HR) estimates and corresponding confidence intervals (CI) for RoPro1 and RoPro2, respectively, are shown (within the standard normal parameter scale). 1B and 1D: "Wordle" plots showing the parameters for RoPro1 and RoPro2, respectively: large fonts correspond to high correlations for the parameters. Parameters are shown by parameter categories lifestyle, host, and tumor. Protective and risk parameters are indicated. The HR for a protective parameter is below 1, indicating that higher levels of the parameter are beneficial. The HR for a harmful parameter is above 1, meaning that the higher the parameter value, the higher the risk. See Table 20 for parameter abbreviations.

[0224] Figure 2A : Shows the probability density function plot of RoPro 1 for patients in the Flatiron Health database compared to patients enrolled in the Phase 1 clinical study BP29428, which investigated imipenem and atezolizumab in patients with solid tumors. The results show a slight rightward shift in the patient population in BP29428, indicating a higher proportion of patients with a poor prognosis. Figure 2B Figure 3: Probability density function plot of RoPro1 from the Flatiron Health database compared to the Phase 1 study BP29428, both limited to primary bladder cancer. The results show a rightward shift in the patient population in BP29428, indicating a higher proportion of patients with a poor prognosis, which in this case may reflect a difference in the number of prior lines of therapy.

[0225] Figure 3A and 3BLongitudinal monitoring of RoPro 1 in response groups in the OAK phase 3 clinical study is shown. The x-axis corresponds to different time points. The start of the first line of therapy (LoT) is the leftmost point, and the date of the outcome event is the rightmost point. In between, time points before the event are shown at 11-day steps. The y-axis corresponds to RoPro 1 (group mean). Each curve represents one of the 5 outcome groups. A: The curves from top to bottom of the graph represent: deceased patients, progressed patients, stable disease patients, and partial response patients, and complete response patients. B: The curves from top to bottom of the graph on the right represent: deceased patients, progressed patients, stable disease patients, partial response patients, and complete response patients. Confidence intervals are shown as shaded bands around each curve. Figure 3 shows that RoPro 1 correlates with treatment response.

[0226] Figure 4A and 4D KM survival curves plotted for patients with high and low RMHS are shown. The upper graph shows the change in probability of survival over time (in days) after the start of first line of therapy (upper and lower curves represent patients with high and low RMHS, respectively). The lower graph shows the change in number of patients with high and low RMHS over time (in days) after the start of first line of therapy. Figure 4B and 4E KM survival curves plotted for patients with high and low RoPro 1 and RoPro 2 are shown, respectively. The upper graph shows the change in probability of survival over time (in days) after the start of first line of therapy (lower curve represents patients in the top 5% for RoPro 1 or RoPro 2, while the upper curve represents patients in the remaining 95% for RoPro 1 or RoPro 2). The lower graph shows the change in number of patients with high and low RoPro 1 or RoPro 2 over time (in days) after the start of first line of therapy. Figure 4C and 4F KM survival curves plotted for patients in deciles of RoPro 1 and RoPro 2 are shown, respectively. The upper graph shows the change in probability of survival over time (in days) after the start of first line of therapy. The curves from top to bottom of the graph represent: deciles 1 to 10. The lower graph shows the change in number of patients in each decile over time (y-axis; from top: deciles 1 to 10) after the start of first line of therapy.

[0227] Figure 5A- D shows the ability of RoPro2 to identify the occurrence of events in longitudinal monitoring. A: shows the RoPro2 score of patients who died (top curve), patients who progressed (middle curve) or patients who had partial or complete response (bottom curve) in the days preceding the corresponding event (death, progression, response). For patients who died, the RoPro2 score worsened significantly towards the event (P = 6.50 x 10-14, Figure 5B ), with a mean score of 0.09 (SD 0.50) at baseline and 0.30 (SD 0.54) at the last measurement before death. The RoPro2 score of patients who progressed also showed a significant, albeit less pronounced (P = 3.90 x 1011, Fig. 5c) worsening towards the event of progression. The RoPro2 score of patients who had partial (n = 191) and complete (n = 11) response did not change significantly (P = 0.23) towards the event of response. Figure 5C Figure 5D DETAILED DESCRIPTION

[0228] An exemplary method of forming the model and score will now be described. It will be appreciated that alternative statistical techniques for analysing the contribution of the parameters to the risk of death can be utilised to form the model. Examples include all of the parameters (i) to (xxvi) or (i) to (xxix), but as described above, the method can be adapted to select only these parameters and / or to incorporate parameters other than (i) to (xxvi) or (i) to (xxix) respectively.

[0229] Two examples of the model and score will now be described as the Roche Prognosis Score (RoPro) 1 and 2. RoPro 1 is a weighted sum of the 26 parameters (i) to (xxvi) of the difference between the patient data and the mean of the corresponding reference parameter, while RoPro 2 is a weighted sum of the 29 parameters (i) to (xxix) of the difference between the patient data and the mean of the corresponding reference parameter. A higher RoPro indicates a higher risk of death and a higher risk of dying, and a lower RoPro indicates a lower risk of death and a lower risk of dying.

[0230] The general formula for the RoPro is ∑ i ln(HR(x i ))(m ij -m i ), where HR(x i ) is the HR estimated for parameter i, mi i is the mean of the parameter in the Flatiron Health database dataset, m ij is the value of parameter i for patient j when i e I. The HR weights the contribution of the parameter to the score, and the subtraction of the mean of the parameter centres the score around zero.

[0231] ​​An example of a generic RoPro1 formula for all indications generated using this method is as follows:

[0232] -0.0364 (Albumin - 38.79) + 0.278 (ECOG - 0.78) + 0.00035 (LDH - 273.99) - 0.00921 (Lymphocytes / 100 white blood cells - 25.83) - 0.0427 (Hemoglobin - 12.28) + 0.00034 (ALP - 108.93) + 0.0549 (NLR - 0.57) - 0.0265 (Chloride - 101.58) + 0.00603 (Heart rate - 83.37) + 0.00373 (AST - 25.98) + 0.0103 (Age - 66.36) + 0.0112 (Urea nitrogen - 17.33) - 0.0162 (Oxygen - 96.42) + 0.109 (TNM stage - 2.98) - 0.0067 (Protein - 69.7) - 0.00296 (Systolic RR - 129.58) - 0.0302 (Eosinophils / 100 white blood cells - 2.32) + 0.0621 (Bilirubin - 0.51) + 0.07228 (Calcium - 9.37) + 0.16482 (Gender - 0.48) - 0.0075 (BMI - 28.25) + 0.256 (Smoking - 0.36) - 0.00048 (Platelets - 272.01) + 0.0531 (Number of metastatic sites - 0.44) - 0.00334 (ALT - 24.63) + 0.00082 (White blood cells - 12.78).

[0233] An example of a generic RoPro2 formula for all indications generated using this method is as follows:

[0234] 0.01012 (age - 66.695) + 0.12264 (gender - 0.502) + 0.20044 (smoking - 0.581) + 0.06476 (number of metastatic sites - 0.163) + 0.23399 (ECOG - 0.834) + 0.09786 (NLR - 0.583) - 0.00801 (BMI - 27.838) - 0.04095 (oxygen - 96.607) - 0.00303 (systolic RR - 128.707) + 0.00521 (heart rate - 83.246) - 0.04637 (Hgb_T0 - 12.092) + 0.00927 (white blood cells - 11.077) + 0.01108 (urea nitrogen - 17.53) + 0.11564 (calcium - 9.314) - 0.00078 (platelets - 272.168) - 0.00623 (lymphocyte to white blood cell ratio - 23.786) + 0.00285 (AST - 26.729) + 0.00117 (ALP - 111.233) - 0.00978 (protein - 69.034) - 0.00252 (ALT - 25.152) - 0.04085 (albumin - 37.798) + 0.17365 (bilirubin - 0.523) - 0.01713 (lymphocytes - 3.683) - 0.00467 (carbon dioxide - 25.768) - 0.02951 (chloride - 101.369) + 0.1176 (monocytes - 0.729) - 0.03171 (eosinophils to white blood cell ratio - 2.225) + 0.00022 (LDH - 286.624) + 0.08136 (tumor stage - 3.101).

[0235] Table 15 shows a description of the parameters in the above formula and an example of how these parameters are measured and evaluated in the RoPro model. It will be appreciated that in other embodiments the parameters can be measured in any other suitable way and different units can be used. The values assigned to non-numerical parameters, such as gender and smoking history, can be chosen freely as long as they are used consistently in the training data and the data input to the patient score. In the example shown in the above formula, females are assigned a value of 0 and males a value of 1. However, if in the entire training data and patient data, females are assigned a value of 1 and males a value of 0, then the generated weight values (ln(HR(x i )) have the opposite sign, so the contribution of this parameter to the score will remain unchanged, and an identical score is generated for the patient. The term gender / sex is used interchangeably herein. This principle also applies to all parameters that can be numerically scaled without affecting the resulting score, as long as this scaling is consistent across the training data and the patient input data.

[0236] In the above formula, ECOG level is entered directly into the model. ECOG levels of 0, 1, 2, 3, and 4 were used. Subjects with an ECOG value of 5 were not included in the training data. As shown in the formula, the mean of the ECOG level training data was 0.78.

[0237] Further, a specific RoPro formula for each cancer indication can be generated by re-estimating the weights (HR(xi i ) in a specific cohort using data from subjects with that specific cancer indication. For the purposes of validation explained below, the universal score was applied in an independent clinical study without re-estimating the parameter weights.

[0238] To calculate a patient’s score, the patient’s measured values for 26 or 29 parameters (albumin, ECOG, LDH, etc.) are entered into the formula. The patient’s measured value for each parameter is inserted into the formula in place of the corresponding parameter label. The patient RoPro1 for patients in the Flatiron Health database used to generate the score ranged from -4.06 to 3.72, with 99% lying in (-2.12; 2.00). The Flatiron Health database will be discussed in more detail later. The values used for each parameter are discussed in Table 15. For example, for the “Sex” parameter, a value of 1 is assigned if the patient is male, and a value of 0 is assigned if the patient is female.

[0239] The 26 factors (i) through (xxvi), and similarly the 29 factors (i) through (xxix), independently contribute to the quantitative prognostic risk score, i.e., “RoPro”.

[0240] RoPro1 and RoPro2 were validated in two independent clinical studies (Phase 1 and Phase 3). Here it was found to be strongly associated with early patient withdrawal from the study (less than 3 days), progression-free survival, and overall survival. Changes in RoPro over time are predictive of subsequent progression and death.

[0241] In the development of RoPro1, the inventors discovered 39 parameters that were significantly associated with overall survival (OS), of which parameters (i) through (xxvi) independently contributed in multivariate modeling (Table 1A, Figure 1A and 1B ). The resulting model was correlated with time to death (r 2 = 0.24), significantly outperforming the RMHS (r 2 = 0.02) on the same data.

[0242] In the development of RoPro2, the inventors similarly discovered that parameters (i) through (xxix) independently contributed in multivariate modeling (Table 1B, Figure 1C and 1D). The resulting model was correlated with time to death (r 2 = 0.30) even better than RMHS (r 2 = 0.02) on the same data.

[0243] Table 1A: Final RoPro1 cox regression model (OS) and RoPro parameter descriptions.

[0244]

[0245]

[0246] * For continuous variables: hazard ratio (HR) per 1 standard deviation (SD) within normal scale, i.e. estimated from standard-normal transformed parameters.

[0247] ** tailHR: HR for patients with a particular high parameter value (equal to 97.5% quantile) compared to patients with a particular low value (equal to 2.5% quantile). Adjusted for other model parameters.

[0248] + Mean

[0249] ++ 48.3% of patients were male

[0250] +++ 36.0% of patients had a clear smoking history

[0251] Table 1B: Final RoPro2 cox regression model (OS) and RoPro parameter descriptions.

[0252]

[0253] 1 Hazard ratio per variable scale (= per measurement unit), and 95% confidence interval

[0254] 2 Patients with parameter values at mean + 2SD vs. patients with values at mean - 2 SD of the mean

[0255] 3 Mean in Flatiron Health data

[0256] 4 Standard deviation in Flatiron Health data

[0257] 5 Coding 1 = male, 0 = female

[0258] 6 Code 1 = smoking history, 0 = no smoking history or unknown

[0259] 7 Code 1 if NLR > 3, 0 if NLR <= 3

[0260] 8 Coding details can be found in online Supplementary Table 1

[0261] RoPro1 and RoPro2 were calculated for each subject, respectively, in the data used to form the RoPro1 and RoPro2 models. Figure 4A and 4B Survival curves plotted for RoPro1 and RMHS are shown. According to Figure 4A low / high RMHS in there is a clear separation of the survival curves HR 2.22 (2.15; 2.28). Figure 4B The plot of RoPro1 in shows a better separation of the survival curves (HR 4.72 (4.57; 4.87), indicating that high RoPro1 is more strongly associated with time to death than high RMHS. Moreover, the survival curves can be shown in fine granularity: the sample can be divided into 10 subgroups of equal size but increasing RoPro1 (10% quantiles). The corresponding Kaplan-Meyer curves show a clear separation, i.e. using RoPro1 not only allows to stratify high- and low-risk patients, but also to assign a well-differentiable level of risk of death. Median survival times are clearly separated across quantiles, with a median survival time of 2286 days for the lowest quantile and 147 days for the highest quantile. The HR for patients from the highest risk group (RoPro > 1.05) compared to the lowest risk group (RoPro < -1.19) is 20.48 (19.47-21.55), P < 2.23x10 -308 .

[0262] RoPro1 and RMHS were applied to each data cohort in the Flatiron Health database, respectively. In all cohorts, the generic RoPro1 was clearly superior to RMHS in the prognosis of time to death. For CLL, the strongest performance improvement was seen for cohort-specific RoPro1 (r 2 = 0.11 for generic RoPro1 r 2 = 0.17 for CLL-specific RoPro1). For metastatic breast cancer, we obtained a particularly strong improvement in the correlation with time to death (from r 2 = 0.10 to r 2 = 0.17) by including the hormone receptor status, HER2 neu status and granulocyte / leukocyte ratio.

[0263] The RoPro1 score performed well when applied to independent datasets (sets not from the Flatiron Health database). Patients with advanced non-small cell lung cancer with elevated RoPro1 (higher than 10%) had a 6.32-fold (95% CI 5.95-6.73) increased risk of death (P < 2.23 x 10-308) compared to patients with low RoPro1 (lower than 10%).

[0264] The RoPro1 showed a strong correlation with time to death (r 2 = 0.001-0.033, depending on cohort) compared to established prognostic scores, such as the Royal Marsden Hospital Score (RMHS) (r 2 = 0.155-0.239, depending on cohort), with a significant improvement.

[0265] Individual patient RoPro2 scores (derived by inputting the measured value of each of the 29 variables into the formula) ranged from -3.22 to 3.61, with 99% of the range between -2.33 and 2.22. Higher scores indicated a worse prognosis for OS.

[0266] Figure 4D , 4E and 4F present a comparison of the prognostic risk scores based on RMHS or RoPro2. Figure 4D Survival curves according to high / low RMHS (HR 2.37; 95% CI 2.32-2.43) show a clear separation. Figure 4E Patients with the highest 10% RoPro2 scores were depicted against the remaining 90% of patients. This analysis according to RoPro2 showed that the separation of survival curves (HR 4.66; 95% CI 4.56-4.77) was better than with RMHS, indicating that a high RoPro2 score was more strongly correlated with time to death than a high RMHS. Furthermore, subdividing the sample into ten subgroups of equal but increasing RoPro2 scores (deciles) showed that the respective Kaplan-Meier survival curves were clearly separated, reflecting a worse OS Figure 4F ) in the higher risk subgroups. Median survival was clearly separated across deciles, with a median survival of 2,975 days for the lowest decile and 118 days for the highest decile. The HR for the highest risk patients (RoPro2 score > 1.13) was 25.79 (95% CI 24.58-27.06) compared to the lowest risk group (score < -1.26), P < 2.23 x 10-308.

[0267] When RoPro2 and RMHS were applied individually to the cohorts, the universal RoPro2 formula again clearly outperformed RMHS in all performance metrics across all cohorts.

[0268] More examples of validation results of RoPro 1 and RoPro 2 on clinical trial data are explained below. In the validation analysis of these two independent clinical studies, the score showed strong correlation with early study withdrawal (within 3 days), PFS, and OS.

[0269] Applied to Phase 1 study

[0270] It is important to have a thorough understanding of the patient population in early oncology clinical trials for conclusive data interpretation and development decisions. RoPro 1 and RoPro 2 were retrospectively applied to patients in Phase 1 first-in-human study BP29428 (NCT02323191), which investigated the safety, pharmacokinetics, and preliminary antitumor activity of emicizumab and atezolizumab combination administration in participants with selected locally advanced or metastatic solid tumors who were not amenable to standard treatment.

[0271] RoPro 1 and RoPro 2 were used to give each patient in the BP29428 study a RoPro 1 and RoPro 2 value, respectively, and to compare each patient’s RoPro 1 and RoPro 2 value to the patient’s outcome in the study to determine the accuracy of RoPro 1 and RoPro 2 in determining the prognosis of the patient.

[0272] The RoPro 1 of patients in the Flatiron Health dataset and patients who participated in BP29428 were approximately normally distributed with a mean of approximately 0( Figure 2A ) and a median of approximately 0. The RoPro 1 distribution of patients who participated in BP29428 tended to extend further to the right, indicating an increase in the proportion of patients in BP29428 who were particularly high risk( Figure 2A ).

[0273] Patients with high RoPro 1 were at higher risk of death, HR = 4.99 (2.59; 9.63), P = 1.5 x 10 -6 ) compared to RMHS, HR = 3.38 (1.79; 6.38), P = 1.72 x 10 -4 ) provided improved discrimination. In addition, RoPro 1 can be used to identify early study withdrawals. Patients with the highest 5% RoPro 1 (n = 11) stayed in the study for an average of only 3 days (Table 16), whereas this discrimination was not possible for RMHS. As shown in Tables 4A and 4B, RoPro 1 quintiles were more effective than RMHS in indicating the length of time in the study.

[0274] In BP29428, primary bladder cancer diagnosis comprised the largest subgroup (n=62). This subgroup had a higher proportion of advanced stage patients compared to the bladder cohort in the Flatiron Health database used to construct RoPro1 (mixed stages), with an average of 1.76 (+ / - 1.00) prior lines of therapy. Thus, the RoPro1 distribution in BP29428 shifted to the right ( Figure 2B ), i.e. there were more patients with high RoPro in the bladder subgroup of BP29428. Nonetheless, in this subgroup, RoPro1 was also strongly associated with OS (P=4.86x10 -7 ). Furthermore, all patients from the highest RoPro1 10th percentile (n=6) remained in the study for only 1 day (Table 17), indicating that RoPro1 was useful even though the number of prior lines of therapy distribution deviated from the Flatiron Health discovery data.

[0275] The association of RoPro2 with OS was replicated in the Phase I study BP29428 (n=217,

[0276] P=4.56x10-14, r 2 =0.22, C-index=0.80).

[0277] Patients with RoPro2 > 1.13 (cut-off value in Flatiron Health equal to 90th percentile, n=11) had a poor OS prognosis (HR 16.36; 95% CI 7.95-33.66), providing better discrimination compared to RMHS (HR 3.38; 95% CI 1.79-6.38).

[0278] One example of the potential application of RoPro2 is its ability to indicate early study withdrawal. Patients with RoPro2 > 1.13 (n=11, see cut-off definition above) all withdrew early from the study due to disease progression or death (average time on study was 29 days). Only one patient remained on the study until the second treatment cycle. Notably, all patients had an ECOG of 1 per study protocol. It would not have been possible to have a similar level of discrimination using RMHS (see Table 18).

[0279] Application to Phase 3 studies

[0280] A retrospective analysis of Phase 3 study results was performed to assess the impact of using RoPro1 and RoPro2 as exclusion criteria and to investigate whether changes in RoPro1 and RoPro2 over time were indicative of subsequent events.

[0281] The OAK Phase III study (Rittmeyer A, et al., 2017) (NCT02008227) evaluated the efficacy and safety of atezolizumab compared to docetaxel in participants (n=1187) with locally advanced or metastatic NSCLC who failed platinum-containing chemotherapy.

[0282] RoPro1 and RoPro2 were used to give a RoPro1 or RoPro2 value to patients in the NCT02008227 study, and to compare each patient's RoPro1 and RoPro2 value to the patient's outcome in the study to determine the accuracy of RoPro1 and RoPro2 in determining the prognosis of the patient.

[0283] Again, RoPro1 (r 2 = 0.20, p = 1.09 x 10 -59 ) was significantly better than RMHS (r 2 = 0.06, p = 2.80 x 10 -18 ) in the prognosis of time to death (Table 2). In addition, RoPro1 was also associated with progression free survival (r 2 = 0.06, p = 1.06 x 10 -17 ).

[0284] We evaluated the potential impact of using a prognostic score on the comparison of OS between treatment arms (Table 2). In the unadjusted analysis, we observed a HR of 0.794 (0.690; 0.913) for atezolizumab vs. control, according to published results (Graf E et al., 1999). Using RoPro1 as a covariate, the effect estimate increased to 0.780 (0.678; 0.898), while adjusting with RMHS slightly attenuated the signal (0.801 [0.692; 0.922]). An improvement in the HR was also observed by excluding patients from the highest 10% of RoPro1 (0.766 [0.659; 0.891]). Despite the reduced sample size, we obtained a higher significance (P = 0.0006) compared to the analysis of the whole data (P = 0.0012).

[0285] Table 2: Impact of using a prognostic score on HR estimate in the post-hoc analysis of the OAK Phase 3 clinical study

[0286]

[0287]

[0288] 1 Correlation with time to death (rSq from cox regression (r 2 )).

[0289] 2 Median of 999 replicated datasets

[0290] The correlation between RoPro2 and OS survival was replicated in the phase III OAK study (n = 1,187, P = 3.65x10-56, r 2 =0.19, C-index=0.68). Patients with RoPro2>0.81 (the cutoff value for dedicated advanced NSCLC RoPro2 in Flatiron Health is equal to the 90% quantile, n=76) had a poorer OS prognosis (HR 3.62; 95% CI 2.82-4.65), which again provided better discrimination compared with RMHS (HR 1.97; 95% CI 1.79-2.31). The area under the curve value was again superior to RMHS. Patients from the high RoPro2 category stayed in the study for an average of only 4.8 months (median 2.4 months) (see Table 19).

[0291] The potential impact of using prognostic scores to compare OS between treatment groups was also evaluated. In an unadjusted analysis, based on published results, an HR of 0.794 (95% CI 0.690–0.913, P = 0.0012) was observed for atezolizumab versus docetaxel. 20 By excluding patients with RoPro2 > 0.81, a lower HR (0.785; 95% CI 0.678–0.909) was observed. Despite the loss of sample size, the significance level (P = 0.0012) was almost the same as that of the analysis of the entire dataset (P = 0.0012).

[0292] As shown in Figure 3, RoPro1 was also shown to be able to distinguish event groups in longitudinal monitoring. RoPro1 was elevated in patients who died 99 days before death, with a mean RoPro1 of 0.32 (SD 0.45), and was significantly higher in patients who died (P = 8.78 × 10 -15 ) increased, with the mean RoPro1 measured before death being 0.77 (SD 0.48). Progression (black) patients showed a significant increase starting 66 days before progression (P = 4.62 x 10 -6 ), from a mean RoPro1 of 0.02 (SD 0.45) to a mean RoPro1 of 0.15 (SD 0.46) at the time of the event. Partial (n = 191 patients) and complete responders (n = 11 patients) started with a low RoPro1 (mean of -0.10, SD 0.40), which did not change significantly towards the response event (P = 0.46).

[0293] Finally, the ability of RoPro2 to discriminate event occurrence in longitudinal monitoring was assessed (Figure 5). For patients who died (top curve), scores significantly worsened towards the event (P = 6.50 x 10-14, Figure 5A , 5B , the mean score at baseline was 0.09 (SD 0.50) and the last measurement before death was 0.30 (SD 0.54). Progressing patients (middle curve) also showed a significant, but less pronounced (P = 3.90 x 10-11, Figure 5A , Figure 5C ). Partial (n = 191) and complete (n = 11) responders (bottom curve) did not significantly change towards the response event Figure 5A .

[0294] As mentioned above, RoPro1 and RoPro2 were validated in two independent clinical studies (Phase 1 and 3), both investigating immunotherapies. Our analysis in study BP29428 (combination treatment with inotuzumab and atezolizumab, NCT02323191) showed that the score not only correlates with OS, but also with specific early study withdrawals (within less than 3 days), as shown in Table 16 for RoPro1. Therefore, using RoPro 1 or RoPro2 to exclude very high-risk patients can help to protect patients from unnecessary study procedure burden and potential adverse events. It can also support fast study conduct and reduce trial costs, while not significantly hindering recruitment, as only a few patients might need to be excluded. Likewise, excluding 10% of patients with the highest RoPro1 or RoPro2 in the OAK Phase 3 trial of monotherapy atezolizumab in NSCLC compared to the chemotherapy control group indeed increased the treatment effect, suggesting that these high-risk patients benefit less from the intervention. Our data show that RoPro1 and RoPro2 are able to more accurately distinguish between high-risk patients with poor physical health status and high risk of withdrawal and patients who can still benefit from study treatment. It is also possible to define a priori cut-offs for patient exclusion / inclusion criteria in order to exclude a pre-specified fraction of patients (e.g. 5%). Investigating RoPro1 over time, we found a steady increasing trend of patients who subsequently progressed or died, while we could not determine a clear correlation between the course of RoPro1 and response. However, a larger number of patients might lead to corresponding findings.

[0295] RoPro1 and RoPro2 are easy to use, despite the use of a large number of parameters, most of which are routinely measured and / or available in clinical routine. These parameters are combined in an easy-to-use simple score. Further, for the calculation of the RoPro of a patient, missing parameters can be tolerated, meaning that even if patient information is incomplete, a score can still be generated. For example, when patient data is missing 5-10 parameters that are included in the model, a useful score can still be generated.

[0296] Further, RoPro1 and RoPro2 can be applied to various cancer indications, as evidenced by the good performance of RoPro1 and RoPro2 in BP29428, where 40% of the patients had a cancer type other than the 12 used to establish the score. Cancer-specific models can be generated using only data from subjects with a specific cancer type, and examples of such models are described in detail below. Cancer-specific models can be superior to general models, for example for CLL and metastatic breast cancer, but general models can still produce useful results.

[0297] Ease of use, applicability across cancer indications, and increased prognostic power further encourage the use of RoPro1 and RoPro2 for cohort comparisons, for example during dose escalation in a FIH study or to interpret study results compared to a real-world setting as shown in BP29428, where the RoPro1 and RoPro2 distribution differs from the Flatiron Health cohort. The determination of the presence of specific high-risk patients helps to interpret the overall study results. Further, the continuous monitoring of RoPro1 over time, possibly by adverse events emerging from treatment or beyond tumor progression, increases the confidence in treatment decisions. Our analysis in OAK showed that high-risk patients with a worsening score over time do not benefit from treatment. The observation of a stable score or even a slightly improved score can indicate a treatment benefit and can - among other considerations - be used to make a decision to continue treatment.

[0298] When applying RoPro1 or RoPro2 to a second- or later-line cohort, additional parameters for the score can include the number and type of previous treatment regimens. As evidenced by our analysis of clinical studies, RoPro1 and RoPro2 are very useful even if the number of previous treatment lines differs between cohorts. Other optional parameters to be included in the model and score can include urine, blood, (epi)genetic biomarkers, and self-reported health (Sudlow C et al., 2015) and fine analysis, for example association with progression-free survival (PFS).

[0299] RoPro 1 and RoPro 2 demonstrate the value of analyzing large patient datasets, leading to a granularity that was previously not possible. Despite the uncertainties and inaccuracies that are typically encountered in retrospective real-world data analyses (Kahn MG et al., 2016), the inventors’ findings suggest that bias has been overcome. This is supported by the high agreement with literature results, the cross-cohort applicability of the scores, and the successful application to independent clinical study data, which produced comparable model fit quality to the Flatiron Health dataset.

[0300] RoPro 1 was obtained by evaluating 131 demographic, clinical, and routine blood parameters within a Cox proportional hazards framework. 99,249 patients came from 12 different cohorts defined by tumor type (non-small cell lung, small cell lung, melanoma, bladder, breast, colorectal, renal cell, ovarian, hepatocellular, multiple myeloma, chronic lymphocytic leukemia, diffuse large B-cell lymphoma). All treatment regimens were included.

[0301] Training data was obtained from the Flatiron Health database, which is derived from electronic health record (EHR) data from over 280 cancer clinics including over 2.1 million active cancer patients in the United States https: / / flatiron.com / real- world-evidence / ),[12-2018]. The Flatiron Health database has longitudinal, demographic, and geographic diversity, which can be advantageous for developing broadly applicable models. Parameters can be numerical (e.g., age) or non-numerical (e.g., gender is female or male). Non-numerical parameters are assigned a numerical value for modeling. As discussed above, the values used for each parameter in the RoPro 1 and RoPro 2 models are discussed in Table 15. For example, for the “gender” parameter, a value of 1 is assigned if the patient is male and a value of 0 is assigned if the patient is female. The values assigned to non-numerical parameters, such as gender and smoking history, can be freely chosen as long as they are used consistently in the training data and the data input to the patient score. Further, numerical values can be scaled without affecting the resulting score, as long as such scaling is consistent across all training data and patient data input to the score.

[0302] The database includes structured data (e.g., laboratory values and prescribed medications) and unstructured data, e.g., data collected through technologically supported chart abstraction from physicians’ notes and other unstructured documents (e.g., biomarker reports).

[0303] The database can be organized according to cohorts defined by cancer type: hepatocellular carcinoma (HCC), advanced melanoma, advanced non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), bladder cancer, metastatic renal cell carcinoma (RCC), metastatic colorectal cancer (CRC), diffuse large B-cell lymphoma (DLBCL), ovarian cancer, metastatic breast cancer, multiple myeloma, chronic lymphocytic leukemia (CLL), and optionally follicular lymphoma, pancreatic cancer, and head and neck cancer. In the Flatiron Health database, patients are similar in age, sex, and race / ethnicity to the US melanoma, NSCLC, and RCC patient populations according to estimates of disease prevalence in the Surveillance, Epidemiology, and End Results data from 2014 [National Cancer Institute. SEER*Stat software, version 8.3.4. http: / / seer.cancer.gov / seerstat accessed February 9, 2018], although in other embodiments of the application, databases including patients with alternative characteristics can be used. This can be advantageous in tailoring models to particular populations. In the Flatiron Health database, all cohort datasets include demographic, clinical data (e.g. cancer type, disease stage, and comorbidities), medication prescription data, and routine blood biomarker data.

[0304] Data released by Flatiron Health in December 2018, including demographic, clinical data (e.g. cancer type, disease stage, and comorbidities), medication prescription data, and routine blood biomarker data were used to form the example RoPro1. A total of 99,249 patients from 12 cohorts were available for analysis: advanced melanoma (n=3,543), advanced non-small cell lung cancer (NSCLC) (n=33,575), bladder cancer (n=4,570), chronic lymphocytic leukemia (CLL) (n=8,904), diffuse large B-cell lymphoma (DLBCL) (n=3,396), hepatocellular carcinoma (HCC) (n=1,028), metastatic breast cancer (n=12,425), metastatic colorectal cancer (CRC) (n=14,487), metastatic renal cell carcinoma (RCC) (n=4,057), multiple myeloma (n=5,345), ovarian cancer (n=3,713), small cell lung cancer (SCLC) (n=4,206). For the final analysis, 42 general measures and 89 additional cohort-specific biomarkers were available. A description of the respective patient characteristics is included in Table 1A. The median follow-up time was 23.3 months (+ / - 21.4 SD) and the median survival time was 20.4 months (95% CI 20.13 to 20.70).

[0305] Flatiron Health released data in May 2019, including demographics, clinical data (such as cancer type, disease stage, and comorbidities), medication prescription data, and routine blood biomarker data were used to form the example RoPro2. A total of 110,538 patients from 15 cohorts were available for analysis: advanced melanoma, advanced non-small cell lung cancer (NSCLC), bladder cancer, chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), hepatocellular carcinoma (HCC), metastatic breast cancer, metastatic colorectal cancer (CRC), metastatic renal cell carcinoma (RCC), multiple myeloma, ovarian cancer, small cell lung cancer (SCLC), follicular lymphoma, pancreatic cancer, and head and neck cancer. For the final analysis, 45 universal measures and 99 other cohort-specific biomarkers were available. A description of the respective patient characteristics is included in Table IB. The median survival time was 18.7 months (95% confidence interval [CI] 18.5-18.9).

[0306] Only parameters available for more than 25% of patients were used, and patients lacking first-line treatment information were excluded.

[0307] To form the model, the overall survival (OS) of patients in the training data was investigated using the Cox proportional hazards model (Cox DR, Journal of the Royal Statistical Society Series B (Methodological) Vol. 34). Survival time was calculated from the start of the patient’s first-line treatment (defined as TO) to the start of the “death” event, as coded in the Real-World Death Rate Table (Curtis MD et al., 2018). For cohorts including only advanced / metastatic patients, the first line was the first advanced / metastatic line of treatment. Censored follow-up time was calculated as the number of days from TO to the date of the patient’s last documented contact with their clinic (visit, drug administration, sample collection, etc.). For the training data used for modeling, we used the last available measurement of the patient prior to TO.

[0308] For all continuous parameters, observations that differed from the mean by more than 4 standard deviations were excluded. Missing data were imputed for each cohort separately using the missForest R package. (R Core Team, 2013). For a sensitivity analysis, the analysis was repeated with cohort parameter means replacing missing patient data.

[0309] Data can be filtered so that only important parameters are included in the model. In forming the example model, as a preliminary filter, each parameter was analyzed individually. Parameters with a p-value less than a1= 0.05 / 45 = 0.0011 (Bonferroni correction) were deemed suitable for modeling and retained in the training data. Parameters were included in the model in the order given by their importance in the filter analysis. To be retained in the model, a parameter needed to be included that produced a significant improvement in the model (P < a1). Parameters that lost significance due to the inclusion of another parameter were removed from the model. By construction, this procedure controlled the family-wise error rate at a = 0.05, i.e., all parameters were significant after multiple testing adjustment.

[0310] For continuous variables, the cox model produces a hazard ratio (HR) that is given for each unit of the survey parameter: for age, for example, the HR is the HR for each 1 year difference in age. Thus, for parameters with high absolute values, the relevance of the HR estimate can appear negligible despite overwhelming statistical evidence. Thus, a "tail HR" is also used. The tail HR is the HR for patients with a particular high parameter value (equal to the 97.5% percentile) compared to patients with a particular low value (equal to the 2.5% percentile). However, the statistical test is based on the full quantitative model.

[0311] Modified RoPros

[0312] As discussed above, the RoPro 1 and RoPro 2 models and scores can be modified by including other parameters or modifying the training data, e.g., including only patients with a particular cancer type. For the cancer type in question, the specific model and score can improve accuracy compared to the general RoPro 1 and RoPro 2 discussed above.

[0313] General RoPro 1 score with cohort covariates

[0314] The score is given by the following formula:

[0315] -0.03644 (albumin - 38.79) + 0.27834 (ECOG - 0.78) + 0.00035 (LDH - 273.99) - 0.00921 (lymphocytes / 100 white blood cells - 25.83) - 0.04271 (hemoglobin - 12.28) + 0.00034 (ALP - 108.93) + 0.05494 (NLR - 0.57) - 0.02653 (chloride - 101.58) + 0.00603 (heart rate - 83.37) + 0.00373 (AST - 25.98) + 0.01032 (age - 66.36) + 0.01123 (urea nitrogen - 17.33) - 0.01622 (oxygen - 96.42) + 0.1092 (TNM stage - 2.98) - 0.00672 (protein - 69.7) - 0.00296 (systolic blood pressure RR - 129.58) - 0.03019 (eosinophils / 100 white blood cells - 2.32) + 0.06207 (bilirubin - 0.51) + 0.07228 (calcium - 9.37) + 0.16482 (gender - 0.48) - 0.0075 (BMI - 28.25) + 0.25569 (smoking - 0.36) - 0.00048 (platelets - 272.01) + 0.05308 (number of sites of metastasis - 0.44) - 0.00334 (ALT - 24.63) + 0.00082 (white blood cells - 12.78) + 0.16954 (advanced NSCLC - 0.34) + 0.20866 (advanced melanoma - 0.04) + 0.33719 (bladder - 0.05) - 0.69241 (CLL - 0.09) - 1.28587 (DLBCL - 0.03) + 0.58248 (HCC - 0.01) - 0.04801 (metastatic breast cancer - 0.13) - 0.0726 (metastatic RCC - 0.04) - 0.76472 (multiple myeloma - 0.05) - 0.47683 (ovarian - 0.04) + 0.20785 (SCLC - 0.04)

[0316] The score takes into account cancer type by including an additional parameter for cancer type in the score.

[0317] Universal RoPro2 score with cohort covariates

[0318] The score is given by the following formula: 0.00942 (age - 66.695) + 0.13976 (gender - 0.502) + 0.2006 (smoking - 0.581) + 0.07037 (number of metastatic sites - 0.163) + 0.22798 (ECOG - 0.834) + 0.09204 (NLR - 0.583) - 0.00734 (BMI - 27.838) - 0.0393 (oxygen - 96.607) - 0.00283 (SBP - 128.707) + 0.00526 (heart rate - 83.246) - 0.04512 (Hgb - 12.092) + 0.00864 (white blood cells - 11.077) + 0.01171 (urea nitrogen - 17.53) + 0.10861 (calcium - 9.314) - 0.00066 (platelets - 272.168) - 0.00614 (ratio of lymphocytes to white blood cells - 23.786) + 0.00342 (AST - 26.729) + 0.00095 (ALP - 111.233) - 0.00885 (protein - 69.034) - 0.00432 (ALT - 25.152) - 0.04085 (albumin - 37.798) + 0.09984 (bilirubin - 0.523) - 0.01623 (lymphocytes - 3.683) - 0.00467 (carbon dioxide - 25.768) - 0.02848 (chloride - 101.369) + 0.10973 (monocytes - 0.729) - 0.02952 (ratio of eosinophils to white blood cells - 2.225) + 0.00055 (LDH - 286.624) + 0.0806 (tumor stage - 3.101) + 0.24079 (advanced NSCLC - 0.309) + 0.17555 (advanced melanoma - 0.032) + 0.23156 (bladder - 0.043) - 0.82379 (CLL - 0.079) - 1.32362 (DLBCL - 0.033) + 0.68852 (HCC - 0.011) - 0.09865 (metastatic breast cancer - 0.107) + 0.06371 (metastatic RCC - 0.036) - 0.78937 (multiple myeloma - 0.057) - 0.50345 (ovarian - 0.035) + 0.28493 (SCLC - 0.04) + 0.33059 (head and neck - 0.039) - 1.79085 (follicular - 0.004) + 0.91282 (pancreatic - 0.045). The score takes into account cancer type by including an additional parameter for cancer type in the score.

[0319] When applied to an independent cohort, it can be interesting to extend the RoPro by specific variables that can be relevant for that cohort. In that case, the extended RoPro can be constructed by fitting a cox model on the cohort data, using the original RoPro and the new variables X as parameters. This yields a formula of the form ln(HR(RoPro))*(RoProj - mean(RoPro)) + ln(HR(X))*(Xj - mean(X)), according to the same principles as used to construct the RoPro formula itself. In this formula, the mean values of RoPro and X are the mean values observed in the study. For the RoPro term, the general formula is used, i.e. the weights of the underlying 29 variables are not re-estimated. Only the weight ln(HR(RoPro)) of the RoPro itself is estimated from the data to determine the correct balance between RoPro and the additional variables X. The extended RoPro formula can then be applied a posteriori to a new sample or a priori to another sample.

[0320] The RoPro distribution can be shifted in a sample with specific inclusion criteria. A typical case can be a study design where all patients take a specific value z for the RoPro variable xi. In this case, the RoPro distribution can be shifted by wi(z-mi), where mi is the mean of xi in the training data and wi is the variable weight in the cox model In(HR). The RoPro cutoff can be shifted by wi(z-mi) to use the RoPro patient exclusion criteria. For example, if a study requires all patients to have ECOG 1, the mean RoPro2 can be shifted by 1.264*(1-0.82)~0.12 compared to the training data (which has a mean ECOG of 0.83). Thus, the RoPro2 cutoff of 1.13 should be replaced by 1.25. As another example, a subgroup of HER2 positive metastatic breast cancer patients has a much better prognosis than other metastatic breast cancer patients, despite the fact that HER2 overexpression is a known risk factor for breast cancer development. This paradox is well known and can be explained by the availability and systematic application of a dedicated targeted therapy. In the RoPro2 application, for patients with positive HER_ status, the corresponding term -0.708*(HER2_ status-0.206) of the RoPro formula (Supplementary Table 7) gives a value of -0.56. Thus, in this case, it can be appropriate to shift the RoPro cutoff by -0.56. It should be noted that the HER2 variable is different from the general RoPro variable, as it directly influences the treatment decision. In the RoPro training data, the vast majority of HER2+ patients receive a targeted therapy (trastuzumab, pertuzumab, etc.). For HER2+ patients who do not receive a targeted therapy, the corresponding RoPro term will be missing. In general, due to the interaction of the HER2 status with the treatment, it can be decided in the actual study design whether it is appropriate to include the HER2 term in the RoPro or not in the comparison of the efficacy.

[0321] Cancer-specific models

[0322] The creation of the specific cancer models and scores discussed below is identical to the general RoPro1 and RoPro2 models described above, with the exception that only training data from the appropriate cancer type is included.

[0323] The RoPro1 score specific for advanced melanoma is given by the following formula:

[0324] -0.03255 (albumin - 39.83) + 0.28838 (ECOG - 0.7) + 0.00046 (LDH - 286.82) - 0.00931 (lymphocytes / 100 white blood cells - 21.7) - 0.03681 (hemoglobin - 13.26) + 0.00119 (ALP - 90.46) + 0.13144 (NLR - 0.61) - 0.02788 (chloride - 102.07) + 0.00564 (heart rate - 80.06) + 0.00412 (AST - 24.45) + 0.00595 (age - 65.15) + 0.0086 (urea nitrogen - 17.68) - 0.04493 (oxygen - 96.89) + 0.12968 (TNM stage - 3.03) - 0.017 (protein - 69.04) - 0.00271 (systolic RR - 131.2) - 0.04086 (eosinophils / 100 white blood cells - 2.53) + 0.00564 (bilirubin - 0.56) + 0.06153 (calcium - 9.34) + 0.14823 (gender - 0.67) - 0.00581 (BMI - 29.07) - 0.00021 (platelets - 255.36) - 0.02629 (number of metastatic sites - 0.99) - 0.0009 (ALT - 25.6) + 0.00519 (white blood cells - 8.2).

[0325] Table 3: Final RoPro cox regression model (OS) for advanced melanoma RoPro 1.

[0326]

[0327] 1 HR = hazard ratio on original scale

[0328] The RoPro 1 score specific to advanced NSCLC is given by the following equation:

[0329] -0.028 (albumin - 37.92) 0.24063 (ECOG - 0.89) 0.00065 (LDH - 265.82) -0.01143 (lymphocytes / 100 leukocytes - 18.89) -0.03854 (hemoglobin - 12.51) +0.00059 (ALP - 105.95) +0.06241 (NLR - 0.78) -0.02719 (chloride - 100.81) +0.0056 (heart rate - 85.61) +0.00315 (AST - 22.29) +0.00746 (age - 67.64) +0.0103 (urea nitrogen - 16.64) -0.02669 (oxygen - 95.74) +0.12445 (TNM stage - 3.31) -0.00811 (protein - 68.83) -0.00285 (systolic RR - 127.43) -0.02494 (eosinophils / 100 leukocytes - 2.35) +0.08866 (bilirubin - 0.47) +0.0813 (calcium - 9.36) +0.19274 (gender - 0.53) -0.00726 (BMI - 27.1) +0.17495 (smoking - 0.87) -0.00033 (platelets - 294.59) +0.08169 (number of metastatic sites - 0.44) -0.00399 (ALT - 23.17) +0.01166 (leukocytes - 9.29) +0.05736 (squamous cells - 0.26) -0.23384 (primary site tumor PDL1 - 0.28) -0.47035 (ALK - 0.03) -0.38758 (EGFR - 0.14) +0.08973 (KRAS - 0.3)

[0330] Table 4: Final RoPro cox regression model (OS) for advanced NSCLC RoPro 1.

[0331] Parameter Unit HR1 [95% CI] p-value Albumin g / L 0.972[0.969；0.976] 1.07E-48 ECOG No 1.272[1.243；1.302] 1.62E-92 LDH U / L 1.001[1.001；1.001] 1.43E-21 Lymphocytes / 100 white blood cells ％ 0.989[0.986；0.991] 6.87E-18 Hemoglobin g / dL 0.962[0.954；0.971] 1.75E-17 ALP U / L 1.001[1.000；1.001] 3.87E-12 NLR No 1.064[1.019；1.112] 5.34E-03 Chloride mmol / L 0.973[0.969；0.977] 4.11E-39 Heart rate bpm 1.006[1.005；1.006] 1.90E-38 AST U / L 1.003[1.001；1.005] 2.84E-04 Age No 1.007[1.006；1.009] 1.26E-20 Urea nitrogen mg / dL 1.010[1.008；1.013] 2.53E-21 Oxygen ％ 0.974[0.967；0.980] 6.02E-15 TNM stage No 1.133[1.113；1.153] 1.09E-42 Protein g / L 0.992[0.989；0.994] 2.29E-10 Systolic RR mmHg 0.997[0.996；0.998] 3.87E-14 Eosinophils / 100 white blood cells ％ 0.975[0.967；0.984] 6.55E-08 Bilirubin mg / dL 1.093[1.023；1.167] 7.92E-03 Calcium mg / dL 1.085[1.058；1.112] 9.13E-11 Gender No 1.213[1.180；1.247] 1.21E-42 BMI kg / m 2 ]] 0.993[0.991；0.995] 1.81E-12 Smoking No 1.191[1.141；1.243] 1.05E-15 Platelets 10*9 / L 1.000[1.000；1.000] 1.92E-05 Number of metastatic sites No 1.085[1.068；1.103] 1.08E-23 ALT U / L 0.996[0.995；0.997] 7.26E-10 White blood cells 10*9 / L 1.012[1.009；1.015] 2.57E-13 Squamous Cell2 No 1.059[1.027；1.092] 2.83E-04 Primary Site Tumor PDL13 No 0.791[0.710；0.883] 2.64E-05 ALK4 No 0.625[0.558；0.700] 5.38E-16 EGFR5 No 0.679[0.643；0.716] 7.37E-45 KRAS6 No 1.094[1.034；1.158] 1.92E-03

[0332] 1 HR = Hazard Ratio on original scale

[0333] 2 Squamous vs non-squamous cell carcinoma

[0334] 3 PDL1 status in primary tumor

[0335] 4 Presence of ALK rearrangement, consistent assessment of blood, tumor site and metastatic sites

[0336] 5 Presence of EGFR mutation, consistent assessment of blood, tumor site and metastatic sites

[0337] 6 The presence of KRAS mutations, consistent assessment of blood, tumor sites, and metastatic sites

[0338] The RoPro1 score specific to bladder cancer is given by the following formula:

[0339] -0.03776 (Albumin - 38.24) + 0.31113 (ECOG - 0.86) - 0.0000006 (LDH - 207.51) - 0.01648 (Lymphocytes / 100 white blood cells - 19.98) - 0.05865 (Hemoglobin - 11.79) + 0.00199 (ALP - 102.19) - 0.02476 (NLR - 0.75) - 0.02689 (Chloride - 101.84) + 0.0067 (Heart rate - 81.99) + 0.01477 (AST - 20.89) + 0.00582 (Age - 70.66) + 0.00883 (Urea nitrogen - 21.95) - 0.01548 (Oxygen - 96.77) - 0.05903 (TNM stage - 3.12) - 0.01324 (Protein - 69.3) - 0.00097 (Systolic RR - 129.7) - 0.0303 (Eosinophils / 100 white blood cells - 2.66) + 0.16313 (Bilirubin - 0.45) + 0.20268 (Calcium - 9.35) + 0.14791 (Gender - 0.74) - 0.0055 (BMI - 27.56) + 0.03807 (Smoking - 0) + 0.00046 (Platelets - 291.95) + 0.08332 (Number of metastatic sites - 0.38) - 0.00943 (ALT - 19.7) + 0.00372 (White blood cells - 8.57) - 0.29635 (Surgery - 0.5) - 0.09292 (N stage - 0.87) + 0.0882 (T stage - 2.5)

[0340] Table 5: Final RoPro cox regression model (OS) for bladder cancer RoPro1.

[0341]

[0342]

[0343] 1 HR = Hazard Ratio on original scale

[0344] 2 Indicates whether the patient underwent a cystectomy or other related surgery

[0345] 3 N stage at initial diagnosis

[0346] 4 T stage at initial diagnosis

[0347] The CLL-specific RoPro1 score is given by the following formula:

[0348] -0.03867 (albumin -41.27) + 0.41112 (ECOG -0.61) + 0.00035 (LDH -257.53) -0.00048 (lymphocytes / 100 white blood cells -67.49) + 0.00858 (hemoglobin -11.9) + 0.00322 (ALP -86.29) + 0.003 (NLR -0.16) -0.0278 (chloride -103.45) + 0.00728 (heart rate -77.6 1) + 0.00348 (AST -24.35) + 0.05572 (age -69.71) + 0.00942 (urea nitrogen -20.19) - 0.07428 (oxygen -96.72) + 0.01033 (TNM stage -1.38) - 0.00604 (protein -65.51) - 0.00352 (shrinkage RR -129.82) + 0.01924 (eosinophils / 100 white blood cells -1.18) + 0.0243 (bilirubin

[0349] -0.64) + 0.04474 (Calcium -9.26) + 0.28007 (Sex -0.62) -0.01238 (BMI -29.11) + 1.28681 (Smoking -0.01) -0.00108 (Platelets -165.63) + 0.6591 (Number of Metastatic Sites -0.01) -0.00752 (ALT -22.11) -0.00108 (WBC -57.72) -0.02779 (Hematocrit -36.55) + 0.01526 (Mono_leuko -6.81)

[0350] +0.4047 (17pDel condition -0.09)

[0351] Table 6: Final RoPro1 cox regression model for CLL RoPro1 (OS).

[0352]

[0353]

[0354] 1 HR = hazard ratio on the original scale

[0355] 2 Patient status of 17p deletion

[0356] The RoPro1 score specific to DLBCL is given by the following equation:

[0357] -0.01324 (Albumin - 38.76) + 0.41084 (ECOG - 0.78) + 0.00051 (LDH - 330) - 0.00259 (Lymphocytes / 100 white blood cells - 21.57) - 0.02629 (Hemoglobin - 12.33) + 0.00246 (ALP - 94.94) - 0.14718 (NLR - 0.67) - 0.01611 (Chloride - 101.62) + 0.00186 (Heart rate - 83.71) + 0.00324 (AST - 27.74) + 0.03897 (Age - 65.41) + 0.0115 (BUN - 18.02) - 0.08422 (Oxygen - 97.03) + 0.11178 (TNM stage - 2.8) - 0.01996 (Protein - 67.65) + 0.00247 (Systolic RR - 129.35) + 0.03503 (Eosinophils / 100 white blood cells - 2.42) - 0.0067 (Bilirubin - 0.6) - 0.08837 (Calcium - 9.45) + 0.21277 (Gender - 0.54) - 0.01197 (BMI - 29.21) - 0.32111 (Smoking - 0) + 0.00116 (Platelets - 264.91) + 0.01659 (Number of metastatic sites

[0358] -0.64) - 0.00427 (ALT - 25.71) + 0.01014 (White blood cells - 8.06) + 0.48816 (BM_

[0359] CD5 - 0.19)

[0360] Table 7: Final RoPro1 cox regression model (OS) for DLCBL RoPro1.

[0361]

[0362] 1 HR = Hazard Ratio on original scale

[0363] 2 CD5 expression status in bone marrow reported by IHC or flow cytometry

[0364] The RoPro1 score specific to HCC is given by the following equation:

[0365] -0.04636 (albumin - 34.77) + 0.09009 (ECOG - 0.85) - 0.00024 (LDH - 242.97) - 0.00747 (lymphocytes / 100 white blood cells - 21.85) - 0.03135 (hemoglobin - 12.56) + 0.0009 (ALP - 190.83) + 0.01655 (NLR - 0.66) - 0.01749 (chloride - 101.53) + 0.00797 (heart rate - 79.59) + 0.00375 (AST - 81.06) + 0.00472 (age - 66.35) + 0.01482 (urea nitrogen - 16.98) - 0.05141 (oxygen - 97.04) + 0.24331 (TNM stage - 3.33) + 0.00045 (protein - 71.86) + 0.00075 (systolic RR - 128.81) - 0.04362 (eosinophils / 100 white blood cells - 2.55) + 0.10363 (bilirubin - 1.23) + 0.07832 (calcium - 9.13) + 0.13751 (gender - 0.8) + 0.00405 (BMI - 27.52) - 0.87098 (smoking - 0.01) + 0.00044 (platelets - 194.03) + 0.02497 (number of metastatic sites - 0.2) - 0.00273 (ALT - 56.13) + 0.0234 (white blood cells - 6.53) + 0.34357 (IsAscites - 0.25)

[0366] Table 8: Final RoPro 1 cox regression model (OS) for HCC RoPro 1.

[0367] Parameter Unit HR 1 [95% CI]]]> p-value Albumin g / L 0.955[0.936；0.974] 6.05E-06 ECOG No 1.094[0.964；1.242] 1.64E-01 LDH U / L 1.000[0.999；1.001] 6.18E-01 Lymphocytes / 100 white blood cells ％ 0.993[0.980；1.006] 2.59E-01 Hemoglobin g / dL 0.969[0.923；1.018] 2.10E-01 ALP U / L 1.001[1.000；1.002] 1.30E-02 NLR No 1.017[0.825；1.253] 8.77E-01 Chloride mmol / L 0.983[0.96；1.006] 1.43E-01 Heart rate bpm 1.008[1.003；1.013] 4.08E-03 AST U / L 1.004[1.002；1.006] 7.55E-05 Age No 1.005[0.996；1.013] 2.62E-01 Urea nitrogen mg / dL 1.015[1.005；1.025] 3.32E-03 Oxygen ％ 0.950[0.897；1.006] 7.67E-02 TNM stage No 1.275[1.134；1.435] 5.05E-05 Protein g / L 1.000[0.989；1.012] 9.38E-01 Systolic RR mmHg 1.001[0.996；1.005] 7.39E-01 Eosinophils / 100 white blood cells ％ 0.957[0.908；1.009] 1.06E-01 Bilirubin mg / dL 1.109[1.019；1.207] 1.67E-02 Calcium mg / dL 1.081[0.911；1.285] 3.72E-01 Gender No 1.147[0.945；1.394] 1.66E-01 BMI kg / m 2 ]] 1.004[0.991；1.018] 5.55E-01 Smoking No 0.419[0.154；1.139] 8.83E-02 Platelets 10*9 / L 1.000[0.999；1.001] 3.61E-01 Number of metastatic sites No 1.025[0.903；1.165] 7.01E-01 ALT U / L 0.997[0.994；1.000] 5.48E-02 White blood cells 10*9 / L 1.024[0.982；1.068] 2.75E-01 IsAscites 2 ]]> ％ 1.410[1.172；1.696] 2.70E-04

[0368] 1 HR = Hazard Ratio on the original scale

[0369] 2 Indicates if the patient had documented evidence of ascites 60 days prior to or within the start of systemic therapy

[0370] The RoPro 1 score specific to metastatic breast cancer is given by the following formula:

[0371] -0.03505 (albumin - 40.26) + 0.27677 (ECOG - 0.65) + 0.00036 (LDH - 279.9) - 0.02201 (lymphocytes / 100 white blood cells - 25.19) - 0.05064 (hemoglobin - 12.57) + 0.0005 (ALP - 116.55) 0.0224 (NLR - 0.39) - 0.00573 (chloride - 102.11) + 0.00426 (heart rate - 84.54) + 0.00453 (AST - 30.73) + 0.00347 (age - 62.48) + 0.0056 (urea nitrogen - 15.74) - 0.00592 (oxygen - 96.52) - 0.05314 (TNM stage - 2.79) - 0.00482 (protein - 69.92) - 0.00228 (systolic RR - 132.42) - 0.04465 (eosinophils / 100 white blood cells - 2.18) + 0.00263 (bilirubin - 0.48) + 0.07961 (calcium - 9.47) - 0.06088 (gender - 0.01) - 0.00505 (BMI - 29.66) + 0.30504 (smoking - 0) - 0.00041 (platelets - 259.77) + 0.01433 (number of metastatic sites - 0.65) - 0.00285 (ALT - 26.76) - 0.01321 (white blood cells - 7.11) - 0.00912 (granulocytes_white blood cells - 65.19) - 0.66376 (status ER - 0.75) - 0.32253 (status PR - 0.21) - 0.64684 (status HER2 - 0.58)

[0372] Table 9: Final RoPro1 cox regression model (OS) for metastatic breast cancer RoPro1.

[0373]

[0374]

[0375] 1 HR = hazard ratio on the original scale

[0376] 2 Estrogen receptor status

[0377] 3 Progestin receptor status

[0378] 4 IHC or flow cytometry reported human epidermal growth factor receptor 2

[0379] The RoPro1 score specific for metastatic CRC is given by the following formula:

[0380] -0.04114 (Albumin - 38.65) + 0.31521 (ECOG - 0.7) + 0.00027 (LDH - 321.84) - 0.01522 (Lymphocytes / 100 white blood cells - 22.63) - 0.01996 (Hemoglobin - 12.03) + 0.00019 (ALP - 141.96) + 0.02894 (NLR - 0.49) - 0.02129 (Chloride - 101.75) + 0.00589 (Heart rate - 82.9) + 0.00545 (AST - 30.64) + 0.01066 (Age - 63.59) + 0.01004 (Urea nitrogen - 15.01) - 0.02764 (Oxygen - 97.15) + 0.11828 (TNM stage - 3.48) - 0.00019 (Protein - 70.32) - 0.0026 (Systolic RR - 130.17) - 0.00342 (Eosinophils / 100 white blood cells - 2.81) + 0.11769 (Bilirubin - 0.55) + 0.05607 (Calcium - 9.31) + 0.08277 (Gender - 0.56) - 0.00752 (BMI - 28.13) + 0.0168 (Smoking - 0) - 0.00067 (Platelets - 298.33) + 0.05164 (Number of metastatic sites - 0.51) - 0.00523 (ALT - 27.14) - 0.00154 (White blood cells - 8.04) + 0.50779 (Status_BRAF - 0.11) + 0.17346 (Status_KRAS - 0.45) + 0.2077 (MSImod_Primary - 0.06)

[0381] Table 10: Final RoPro1 cox regression model (OS) for metastatic CRC RoPro1.

[0382]

[0383]

[0384] 1 HR = Hazard Ratio on original scale

[0385] 2 Presence of BRAF mutation

[0386] 3 Presence of KRAS mutation

[0387] 4 MSI-H and absence of MMR protein expression assessed in primary tissue grouped into one category

[0388] The RoPro1 score specific to metastatic RCC is given by the following formula:

[0389] -0.04107 (Albumin - 38.71) + 0.22807 (ECOG - 0.81) + 0.00066 (LDH - 239.9) - 0.0166 (Lymphocytes / 100 white blood cells - 21.81) - 0.03591 (Hemoglobin - 12.34) + 0.00042 (ALP - 102.79) - 0.07151 (NLR - 0.59) - 0.04034 (Chloride - 101.67) + 0.00675 (Heart rate - 80.99) + 0.01167 (AST - 21.98) + 0.00478 (Age - 65.5) + 0.01264 (Urea nitrogen - 20.71) - 0.00236 (Oxygen - 96.65) + 0.10591 (TNM stage - 3.11) - 0.01258 (Protein - 70.59) - 0.00152 (Systolic RR - 130.68) - 0.03656 (Eosinophils / 100 white blood cells - 2.54) - 0.08249 (Bilirubin - 0.5) + 0.05246 (Calcium - 9.46) - 0.01377 (Gender - 0.7) - 0.00665 (BMI - 30.17) - 0.04439 (Smoking - 0.57) - 0.00052 (Platelets - 286.2) + 0.03115 (Number of sites of metastasis - 0.71) - 0.01082 (ALT - 23.41) + 0.03307 (White blood cells - 8.02) - 0.40166 (Nephrectomy - 0.68) - 0.37353 (Clear cell - 0.7)

[0390] Table 11: Final RoPro1 cox regression model (OS) for metastatic RCC RoPro1.

[0391] Parameter Unit HR 1 [95% CI]]]> p-value Albumin g / L 0.960[0.949；0.971] 3.52E-12 ECOG No 1.256[1.166；1.354] 2.27E-09 LDH U / L 1.001[1.000；1.001] 3.81E-03 Lymphocytes / 100 white blood cells ％ 0.984[0.976；0.991] 2.45E-05 Hemoglobin g / dL 0.965[0.938；0.993] 1.35E-02 ALP U / L 1.000[0.999；1.001] 3.71E-01 NLR No 0.931[0.832；1.041] 2.11E-01 Chloride mmol / L 0.960[0.948；0.973] 4.05E-09 Heart rate bpm 1.007[1.004；1.010] 6.23E-06 AST U / L 1.012[1.007；1.016] 5.35E-07 Age No 1.005[1.000；1.009] 4.25E-02 Urea nitrogen mg / dL 1.013[1.007；1.018] 3.35E-06 Oxygen ％ 0.998[0.971；1.025] 8.63E-01 TNM stage No 1.112[1.054；1.173] 1.00E-04 Protein g / L 0.988[0.980；0.995] 1.44E-03 Systolic RR mmHg 0.998[0.996；1.001] 2.08E-01 Eosinophils / 100 white blood cells ％ 0.964[0.933；0.996] 2.64E-02 Bilirubin mg / dL 0.921[0.764；1.109] 3.85E-01 Calcium mg / dL 1.054[0.980；1.133] 1.56E-01 Gender No 0.986[0.899；1.082] 7.71E-01 BMI kg / m 2 ]]> 0.993[0.988；0.999] 1.94E-02 Smoking No 0.957[0.879；1.041] 3.04E-01 Platelets 10*9 / L 0.999[0.999；1.000] 4.48E-02 Number of metastatic sites No 1.032[0.993；1.071] 1.05E-01 ALT U / L 0.989[0.986；0.993] 1.74E-08 White blood cells 10*9 / L 1.034[1.015；1.053] 3.79E-04 Nephrectomy 2 ]] No 0.669[0.598；0.748] 2.01E-12 Clear cells 3 ]]> No 0.688[0.628；0.754] 1.55E-15

[0392] 1 HR = Hazard Ratio on original scale

[0393] 2 Whether the patient had a nephrectomy

[0394] 3 Clear cell cancer yes / no

[0395] The score specific to multiple myeloma is given by the following formula:

[0396] -0.03132 (albumin -37.7) + 0.40645 (ECOG -0.83) + 0.00093 (LDH -203.06) -0.00781 (lymphocytes / 100 white blood cells -29.79) -0.04835 (hemoglobin -10.98) + 0.00219 (ALP -82.55) -0.07473 (NLR-0.3)-0.01956(chloride-102.06)+0.00436(heart rate-80.65)+0.00191(AST-24.06)+0.03613(age-68.6)+0.01272(urea nitrogen-22.03)-0.01123(oxygen-97.07)+0.23461(TNM

[0397] Stage -1.97) -0.00372 (Protein -82.13) -0.00398 (Shrinkage RR -134.69) -0.04269 (Eosinophils / 100 WBCs -2.42) +0.30565 (Bilirubin -0.5) +0.01936 (Calcium -9.44) +0.13929 (Sex -0.54) -0.00452 (BMI -29.26) +0.9616 (Smoking -0) -0.00132 (Platelets -223.01) + 0.05053 (Number of metastatic sites -0.09) -0.00309 (ALT -23.53) + 0.00522 (WBC -6.95) + 0.3403 (Confirmed abnormalities -0.26) -0.23267 (M-protein IgA -0.19) -0.23776 (M-protein IgG -0.54) -0.45875 (Light chain kappa -0.56) -0.32882 (Light chain lambda -0.34)

[0398] Table 12: Final RoPro1 cox regression model for RoPro1 in multiple myeloma (OS).

[0399]

[0400]

[0401] 1 HR = hazard ratio on the original scale

[0402] 2 Testing to see if the gene is abnormal

[0403] 3 Is the immunoglobulin class of the patient's M protein IgA?

[0404] 4 Is the immunoglobulin class of the patient's M protein IgG?

[0405] 5 Whether the light chain involved in the patient is kappa

[0406] 6 Whether the light chain involved in the patient is lambda

[0407] The RoPro1 score specific to ovarian cancer is given by the following formula:

[0408] -0.03337 (Albumin - 38.74) + 0.35052 (ECOG - 0.77) + 0.00022 (LDH - 261.65) - 0.00104 (Lymphocytes / 100 white blood cells - 23.59) - 0.01649 (Hemoglobin - 11.84) + 0.0017 (ALP - 92.92) 0.0684 (NLR - 0.49) + 0.00501 (Chloride - 101.92) + 0.00396 (Heart rate - 84.27) + 0.00725 (AST - 23.74) + 0.01471 (Age - 64.54) + 0.02218 (Urea nitrogen - 15.47) - 0.00578 (Oxygen - 96.68) + 0.46146 (TNM stage - 3.13) - 0.00401 (Protein - 69.42) - 0.00307 (Systolic RR - 128.24) - 0.06112 (Eosinophils / 100 white blood cells - 2.53) - 0.11501 (Bilirubin - 0.4) + 0.07324 (Calcium - 9.38) + 0.00015 (Gender - 0) + 0.58224 (BMI - 28.63) - 0.00098 (Smoking - 0) + 0.06139 (Platelets - 333.29) - 0.00837 (Number of metastatic sites - 0.21) + 0.01713 (ALT - 21.46) - 0.3742 (White blood cells - 0.82) + 0.41366 (Clear cell - 0.07)

[0409] Table 13: Final RoPro1 cox regression model (OS) for ovarian cancer RoPro1.

[0410]

[0411]

[0412] 1 HR = Hazard Ratio on original scale

[0413] 2 Degree of debulking after surgical treatment of the patient initially diagnosed with ovarian cancer

[0414] 3 Clear cell carcinoma is / yes

[0415] The RoPro1 score specific to SCLC is given by the following equation:

[0416] -0.01875 (Albumin - 38.7) + 0.14992 (ECOG - 0.94) + 0.00018 (LDH - 349.5) - 0.00919 (Lymphocytes / 100 white blood cells - 21.1) - 0.01399 (Hemoglobin - 12.88) - 0.00014 (ALP - 112.17) + 0.00145 (NLR - 0.7) - 0.01345 (Chloride - 99.97) + 0.00372 (Heart rate - 85.7) + 0.00486 (AST - 29.1) + 0.01048 (Age - 66.8) + 0.01096 (Urea nitrogen - 16.02) - 0.00712 (Oxygen - 95.65) + 0.28747 (TNM stage - 3.55) - 0.00597 (Protein - 68.55) - 0.0045 (Systolic RR - 128.2) - 0.02105 (Eosinophils / 100 white blood cells - 1.99) - 0.07518 (Bilirubin - 0.5) + 0.00318 (Calcium - 9.36) + 0.16448 (Gender - 0.48) - 0.01073 (BMI - 27.94) + 0.11721 (Smoking - 0.98) - 0.00127 (Platelets - 274.14) + 0.02463 (Number of metastatic sites - 0.38) - 0.00172 (ALT - 27.89) - 0.00026 (White blood cells - 9) + 0.58755 (SCLC stage - 0.65)

[0417] Table 14: Final RoPro1 cox regression model (OS) for SCLC RoPro1.

[0418] Parameter Unit HR 1 [95% CI]] p-value Albumin g / L 0.981[0.970；0.993] 1.13E-03 ECOG No 1.162[1.093；1.235] 1.45E-06 LDH U / L 1.000[1.000；1.000] 4.73E-02 Lymphocytes / 100 white blood cells ％ 0.991[0.985；0.997] 3.43E-03 Hemoglobin g / dL 0.986[0.963；1.010] 2.56E-01 ALP U / L 1.000[0.999；1.000] 5.83E-01 NLR No 1.001[0.899；1.115] 9.79E-01 Chloride mmol / L 0.987[0.978；0.996] 4.27E-03 Heart rate bpm 1.004[1.001；1.006] 3.31E-03 AST U / L 1.005[1.002；1.008] 4.17E-04 Age No 1.011[1.006；1.015] 1.04E-05 Urea nitrogen mg / dL 1.011[1.005；1.017] 4.16E-04 Oxygen ％ 0.993[0.971；1.015] 5.28E-01 TNM stage No 1.333[1.223；1.453] 6.12E-11 Protein g / L 0.994[0.986；1.002] 1.26E-01 Systolic RR mmHg 0.996[0.993；0.998] 1.31E-05 Eosinophils / 100 white blood cells ％ 0.979[0.947；1.012] 2.12E-01 Bilirubin mg / dL 0.928[0.807；1.067] 2.91E-01 Calcium mg / dL 1.003[0.916；1.098] 9.45E-01 Gender No 1.179[1.090；1.275] 3.70E-05 BMI kg / m 2 ]] 0.989[0.984；0.995] 1.83E-04 Smoking No 1.124[0.871；1.452] 3.69E-01 Platelets 10*9 / L 0.999[0.998；0.999] 8.66E-09 Number of metastatic sites No 1.025[0.978；1.074] 3.00E-01 ALT U / L 0.998[0.996；1.001] 1.76E-01 White blood cells 10*9 / L 1.000[0.989；1.010] 9.60E-01 SCLC staging 2 ]]> No 1.800[1.594；2.032] 2.34E-21

[0419] 1 HR = Hazard Ratio on the original scale

[0420] 2 Extensive disease vs. limited disease by clinical assessment

[0421] The cohort-specific RoPro2 models show that the consistency of variable correlations between cohorts is generally high. For chronic lymphocytic leukemia (CLL), the strongest performance improvement in cohort-specific re-estimated variable weights was seen (r 2 = 0.12, C-index = 0.70, 3-month AUC = 0.81; for CLL-specific RoPro2, r 2= 0.17, C-index = 0.74, 3-month AUC = 0.83). The metastatic breast cancer model showed the largest improvement by incorporating cancer-specific biomarkers (hormone receptor status and human epidermal growth factor receptor 2 [HER2]-neu status) (for the general RoPro2, r 2 = 0.12, C-index = 0.66, 3-month AUC = 0.83; for the specific RoPro2, r 2 = 0.21, C-index = 0.72, 3-month AUC = 0.83).

[0422] The RoPro2 score specific to advanced NSCLC is given by the following formula: 0.00653 (age - 67.981) + 0.16544 (gender - 0.537) + 0.29991 (smoking - 0.877) + 0.12237 (number of metastatic sites - 0.174) + 0.19453 (ECOG - 0.922) + 0.02572 (NLR - 0.766) - 0.00725 (BMI - 26.899) - 0.03658 (oxygen - 95.996) - 0.00259 (SBP - 126.983) + 0.00518 (heart rate - 85.716) - 0.04524 (Hgb - 12.327) + 0.00529 (white blood cells - 9.807) + 0.01222 (urea nitrogen - 17.129) + 0.14336 (calcium - 9.323) - 0.00051 (platelets - 298.177) - 0.01381 (lymphocytes in blood / 100 white blood cells - 16.727) + 0.00251 (AST - 22.414) + 0.00144 (ALP - 102.824) - 0.01326 (protein - 68.696) - 0.00416 (ALT - 23.117) - 0.03692 (albumin - 37.062) + 0.18286 (bilirubin - 0.467) + 0.01683 (lymphocytes - 1.465) - 0.00542 (carbon dioxide - 26.047) - 0.03064 (chloride - 100.545) + 0.11354 (monocytes - 0.672) - 0.03235 (eosinophils in blood / 100 white blood cells - 2.07) + 0.00055 (LDH - 280.888) + 0.10307 (tumor stage - 3.465) + 0.05508 (squamous cells - 0.269) - 0.18049 (primary site tumor PDL1 - 0.278).

[0423] The RoPro2 score specific to advanced melanoma is given by the formula: 0.0062(age - 65.539) + 0.1059(sex - 0.68) - 0.13496(smoking - 0.352) - 0.01106(number of metastatic sites - 0.368) + 0.2725(ECOG - 0.758) + 0.10207(NLR - 0.594) - 0.00161(BMI - 28.896) - 0.03303(oxygen - 97.009) - 0.0029(SBP - 130.345) + 0.00699(heart rate - 79.392) - 0.02029(Hgb - 13.099) - 0.00206(white blood cells - 8.308) + 0.01633(urea nitrogen - 18.031) + 0.08373(calcium - 9.297) - 0.00025(platelets - 258.758) - 0.01065(lymphocytes in blood / 100 white blood cells - 21.258) + 7e-04(AST - 24.439) + 0.00058(ALP - 94.702) - 0.01989(protein - 68.102) + 0.00155(ALT - 25.62) - 0.04059(albumin - 38.916) + 0.03968(bilirubin - 0.564) + 0.0433(lymphocytes - 1.674) + le-04(carbon dioxide - 25.859) - 0.02939(chloride - 102.027) + 0.09766(monocytes - 0.624) - 0.0515(eosinophils in blood / 100 white blood cells - 2.692) + 0.00068(LDH - 313.076) + 0.13055(tumor stage - 3.017).

[0424] The RoPro2 score specific to bladder cancer is given by the formula: 0.00347 (age - 71.109) + 0.07937 (gender - 0.748) + 0.05865 (smoking - 0.73) + 0.14505 (number of metastatic sites - 0.144) + 0.23068 (ECOG - 0.895) + 0.023 (NLR - 0.692) - 0.00651 (BMI - 27.624) - 0.00666 (oxygen - 96.81) - 7e-04 (SBP - 128.949) + 0.00511 (heart rate - 81.449) - 0.04131 (Hgb - 11.637) + 0.0192 (white blood cells - 8.918) + 0.01188 (urea nitrogen - 22.192) + 0.16766 (calcium - 9.292) - 0.00095 (platelets - 286.043) - 0.01657 (lymphocytes in blood / 100 white blood cells - 18.953) + 0.01467 (AST - 22.276) + 0.00247 (ALP - 106.899) - 0.01493 (protein - 68.58) - 0.00939 (ALT - 20.732) - 0.04466 (albumin - 37.235) + 0.00392 (bilirubin - 0.464) + 0.00895 (lymphocytes - 1.524) - 0.01209 (carbon dioxide - 24.819) - 0.02813 (chloride - 101.957) + 0.06395 (monocytes - 0.661) - 0.02002 (eosinophils in blood / 100 white blood cells - 2.567) - 3e-05 (LDH - 233.144) - 0.07393 (tumor stage - 3.532) - 0.26819 (surgery - 0.504) + 0.11056 (T stage - 2.465).

[0425] The RoPro2 score specific to CLL (Chronic Lymphocytic Leukemia) is given by the following formula: 0.05779 (age - 69.951) + 0.26067 (gender - 0.626) + 0.71525 (smoking - 0.35) + 0.70923 (number of metastatic sites - 0.008) + 0.39314 (ECOG - 0.622) - 0.22371 (NLR - 0.077) - 0.00915 (BMI - 28.769) - 0.01203 (oxygen - 96.811) - 0.00232 (SBP - 129.642) + 0.00826 (heart rate - 77.465) - 0.07827 (Hgb - 11.767) + 0.00096 (white blood cells - 40.123) + 0.00878 (urea nitrogen - 20.195) + 0.01911 (calcium - 9.216) - 0.00137 (platelets - 160.155) - 0.00201 (lymphocytes in blood / 100 white blood cells - 67.021) + 0.00313 (AST - 24.08) + 0.00352 (ALP - 87.048) - 0.00622 (protein - 65.126) - 0.00792 (ALT - 21.752) - 0.03883 (albumin - 40.74) + 0.0879 (bilirubin - 0.621) - 0.00713 (lymphocytes - 28.523) + 0.01213 (carbon dioxide - 25.898) - 0.03209 (chloride - 103.766) + 0.05868 (monocytes - 1.885) + 0.03505 (eosinophils in blood / 100 white blood cells - 1.16) + 0.00037 (LDH - 273.307) - 0.00708 (tumor stage - 1.392).

[0426] The RoPro2 score specific to DLBCL (diffuse large B-cell carcinoma) is given by the following formula: 0.038(age - 66.079) + 0.22192(sex - 0.547) - 0.22649(smoking - 0.351) + 0.11979(number of metastatic sites - 0.012) + 0.31201(ECOG - 0.784) + 0.00719(NLR - 0.631) - 0.01538(BMI - 28.581) - 0.02739(oxygen - 96.915) + 0.00373(SBP - 128.501) + 0.00047(heart rate - 83.492) - 0.02201(Hgb - 12.038) - 0.01588(white blood cells - 7.943) + 0.02116(urea nitrogen - 18.144) - 0.007(calcium - 9.344) - 0.00098(platelets - 260.621) - 0.01131(lymphocytes in blood / 100 white blood cells - 21.008) + 0.00335(AST - 26.771) + 0.00298(ALP - 95.829) - 0.01163(protein - 66.364) - 0.01085(ALT - 24.473) - 0.02293(albumin - 37.656) + 0.17582(bilirubin - 0.563) + 0.10388(lymphocytes - 1.589) + 0.00502(carbon dioxide - 25.833) - 0.01428(chloride - 101.524) + 0.22388(monocytes - 0.638) + 0.00275(eosinophils in blood / 100 white blood cells - 2.293) - 6e-04(LDH - 329.933) + 0.06839(tumor stage - 2.791) + 0.44252(BM_CD5 - 0.168).

[0427] The RoPro2 score specific to HCC (Hepatocellular Carcinoma) is given by the following formula: 0.00592 (age - 66.277) + 0.12226 (gender - 0.807) - 19.43777 (smoking - 0.352) - 0.00323 (number of metastatic sites - 0.094) + 0.10789 (ECOG - 0.922) + 0.15643 (NLR - 0.592) + 0.00388 (BMI - 27.783) - 0.01924 (oxygen - 97.2) - 0.00132 (SBP - 128.568) + 0.00801 (heart rate - 80.125) - 0.01479 (Hgb - 12.489) + 0.06616 (white blood cells - 6.7) + 0.02277 (urea nitrogen - 16.886) + 0.10928 (calcium - 9.092) + 0.00019 (platelets - 195.961) - 0.00532 (lymphocytes in blood / 100 white blood cells - 21.082) + 0.00517 (AST - 68.984) + 0.00093 (ALP - 189.229) - 0.00514 (protein - 71.962) - 0.00396 (ALT - 50.691) - 0.04574 (albumin - 34.109) + 0.36517 (bilirubin - 1.154) - 0.0059 (lymphocytes - 1.339) - 0.03001 (carbon dioxide - 24.955) - 0.02278 (chloride - 101.52) - 0.28334 (monocytes - 0.591) - 0.05039 (eosinophils in blood / 100 white blood cells - 2.729) + 0.00134 (LDH - 306.135) + 0.15332 (tumor stage - 3.111).

[0428] The RoPro2 score specific to metastatic breast cancer is given by the formula: 0.00269 (age - 62.989) + 0.01453 (gender - 0.011) + 0.1938 (smoking - 0.351) + 0.00116 (number of metastatic sites - 0.339) + 0.2144 (ECOG - 0.76) + 0.13996 (NLR - 0.449) - 0.00682 (BMI - 29.567) - 0.03728 (oxygen - 96.722) - 0.00404 (SBP - 131.38) + 0.0038 (heart rate - 85.049) - 0.05297 (Hgb - 12.364) - 0.02052 (white blood cells - 7.427) + 0.00939 (urea nitrogen - 15.999) + 0.10216 (calcium - 9.434) - 0.00071 (platelets - 266.78) - 0.00192 (lymphocytes per 100 white blood cells in blood - 23.863) + 0.00719 (AST - 31.774) + 0.00065 (ALP - 120.469) - 0.00802 (protein - 69.839) - 0.00303 (ALT - 28.294) - 0.0403 (albumin - 39.387) - 0.03278 (bilirubin - 0.49) - 0.0146 (lymphocytes - 1.686) - 0.00091 (carbon dioxide - 25.745) - 0.01655 (chloride - 101.831) + 0.34615 (monocytes - 0.532) - 0.03497 (eosinophils per 100 white blood cells in blood - 2.15) + 0.00051 (LDH - 302.721) - 0.05209 (tumor stage - 2.853) + 0.00353 (N24_T0 - 66.322) - 0.67362 (status ER - 0.752) - 0.31455 (status PR - 0.576) - 0.70817 (status HER2 - 0.206).

[0429] The RoPro2 score specific to metastatic CRC (colorectal cancer) cancer is given by the following formula: 0.01002 (age - 63.765) + 0.05288 (gender - 0.558) + 0.01467 (smoking - 0.351) + 0.04867 (number of metastatic sites - 0.184) + 0.2995 (ECOG - 0.724) + 0.12341 (NLR - 0.561) - 0.00452 (BMI - 28.025) - 0.04628 (N28_T0 - 97.301) - 0.00216 (SBP - 129.373) + 0.00387 (heart rate - 82.507) - 0.00925 (Hgb - 11.801) - 0.00807 (white blood cells - 8.157) + 0.01254 (urea nitrogen - 15.177) + 0.07242 (calcium - 9.242) - 0.00045 (platelets - 294.471) - 0.00861 (lymphocytes in blood / 100 white blood cells - 21.651) + 0.00332 (AST - 31.853) + 0.00074 (ALP - 139.89) - 0.00212 (protein - 69.152) - 0.00485 (ALT - 26.673) - 0.04476 (albumin - 37.617) + 0.22807 (bilirubin - 0.539) - 0.02575 (lymphocytes - 1.635) - 0.01573 (carbon dioxide - 25.449) - 0.03205 (chloride - 101.807) + 0.1334 (monocytes - 0.647) - 0.004 (eosinophils in blood / 100 white blood cells - 2.868) + 0.00044 (LDH - 339.711) + 0.09735 (tumor stage - 3.496) + 0.53129 (status_BRAF - 0.107) + 0.18914 (status_KRAS - 0.451).

[0430] The RoPro2 score specific to metastatic RCC (renal cell carcinoma) is given by the formula: 0.00638 (age - 66.118) - 0.01476 (gender - 0.699) + 0.00179 (smoking - 0.572) + 0.06387 (number of metastatic sites - 0.272) + 0.16002 (ECOG - 0.86) + 0.05433 (NLR - 0.575) - 0.01004 (BMI - 29.805) - 0.0425 (N28_T0 - 96.707) - 0.00266 (SBP - 130.368) + 0.00795 (heart rate - 81.061) - 0.00346 (Hgb - 12.161) + 0.04464 (white blood cells - 8.072) + 0.0106 (urea nitrogen - 21.186) + 0.04739 (calcium - 9.416) - 0.00033 (platelets - 286.676) - 0.00984 (lymphocytes / 100 white blood cells in blood - 21.154) + 0.00739 (AST - 22.983) + 0.00049 (ALP - 109.25) - 0.01171 (protein - 69.997) - 0.01121 (ALT - 24.263) - 0.05349 (albumin - 37.749) - 0.0202 (bilirubin - 0.499) - 0.04447 (lymphocytes - 1.578) - 0.00319 (carbon dioxide - 25.558) - 0.03636 (chloride - 101.488) - 0.0937 (monocytes - 0.626) - 0.03918 (eosinophils / 100 white blood cells in blood - 2.579) + 0.00121 (LDH - 261.093) + 0.0851 (tumor stage - 3.145) - 0.31275 (nephrectomy - 0.66) - 0.37341 (clear cell RCC - 0.695).

[0431] The RoPro2 score specific to multiple myeloma is given by the formula: 0.03943 (age - 68.434) + 0.14741 (gender - 0.542) - 0.09141 (smoking - 0.351) + 0.10112 (number of metastatic sites - 0.037) + 0.32641 (ECOG - 0.901) - 0.05074 (NLR - 0.267) - 0.00195 (BMI - 29.017) - 0.02522 (oxygen - 97.041) - 0.00427 (SBP - 133.73) + 0.00449 (heart rate - 80.919) - 0.0559 (Hgb - 10.824) - 0.00576 (white blood cells - 6.364) + 0.01195 (urea nitrogen - 21.864) + 0.00914 (calcium - 9.33) - 0.00135 (platelets - 220.519) - 0.00925 (lymphocytes / 100 white blood cells in blood - 28.37) + 0.00706 (AST - 23.754) + 0.00196 (ALP - 84.221) - 0.00472 (protein - 78.166) - 0.00766 (ALT - 23.431) - 0.03242 (albumin - 36.515) + 0.26533 (bilirubin - 0.493) + 0.02637 (lymphocytes - 1.75) - 0.003 (carbon dioxide - 24.927) - 0.0161 (chloride - 101.955) + 0.19518 (monocytes - 0.507) - 0.02945 (eosinophils / 100 white blood cells in blood - 2.3) + 0.00071 (LDH - 216.938) + 0.17142 (tumor stage - 1.987) - 0.12075 (M protein IgG - 0.542) - 0.46168 (light chain kappa - 0.572) - 0.38144 (light chain lambda - 0.337).

[0432] The RoPro2 score specific to ovarian cancer is given by the formula: 0.00998 (age - 65.176) + 1.02303 (smoking - 0.35) + 0.11866 (number of metastatic sites - 0.082) + 0.25502 (ECOG - 0.778) + 0.21763 (NLR - 0.555) - 0.00348 (BMI - 28.264) - 0.05212 (oxygen - 96.969) - 0.00274 (SBP - 128.397) + 0.00062 (heart rate - 84.888) - 0.02728 (Hgb - 11.746) + 0.01832 (white blood cells - 7.828) + 0.02424 (urea nitrogen - 15.932) + 0.00599 (calcium - 9.323) - 0.00101 (platelets - 332.437) + 0.01093 (lymphocytes in blood / 100 white blood cells - 21.544) + 0.00837 (AST - 23.971) + 0.00353 (ALP - 91.691) - 0.01219 (protein - 68.922) - 0.01376 (ALT - 21.587) - 0.03487 (albumin - 37.78) + 0.32377 (bilirubin - 0.412) - 0.03305 (lymphocytes - 1.526) + 0.01351 (carbon dioxide - 25.341) - 0.01256 (chloride - 102.004) + 0.05714 (monocytes - 0.539) - 0.03722 (eosinophils in blood / 100 white blood cells - 2.117) + 0.00109 (LDH - 288.609) + 0.40066 (tumor stage - 3.108).

[0433] The RoPro2 score specific to SCLC (small cell lung cancer) is given by the formula: 0.00891 (age - 67.252) + 0.22435 (gender - 0.481) + 0.10511 (smoking - 0.985) + 0.01216 (number of metastatic sites - 0.13) + 0.13932 (ECOG - 0.983) + 0.00473 (NLR - 0.645) - 0.00942 (BMI - 27.722) - 0.03779 (oxygen - 95.68) - 0.00509 (SBP - 127.436) + 0.00184 (heart rate - 85.411) - 0.02368 (Hgb - 12.674) + 0.0187 (white blood cells - 9.25) + 0.00619 (urea nitrogen - 16.446) + 0.04426 (calcium - 9.3) - 0.00107 (platelets - 282.204) - 0.00278 (lymphocytes / 100 white blood cells in blood - 19.709) + 0.0059 (AST - 31.42) + 0.00047 (ALP - 114.166) - 0.00912 (protein - 67.956) - 0.00551 (ALT - 29.195) - 0.02229 (albumin - 37.454) - 0.06248 (bilirubin - 0.52) - 0.02092 (lymphocytes - 1.707) - 0.00089 (carbon dioxide - 26.303) - 0.01935 (chloride - 99.666) - 0.03149 (monocytes - 0.704) - 0.0199 (eosinophils / 100 white blood cells in blood - 1.961) + 0.00069 (LDH - 365.223) + 0.23314 (tumor stage - 3.549) + 0.47876 (SCLC stage - 0.648).

[0434] The RoPro2 score specific to head and neck cancer is given by the formula: 0.00518 (age - 64.674) + 0.12697 (gender - 0.772) - 0.02112 (smoking - 0.805) + 0.09328 (number of metastatic sites - 0.111) + 0.16008 (ECOG - 0.894) - 0.04024 (NLR - 0.775) - 0.00764 (BMI - 24.671) - 0.03521 (oxygen - 96.753) - 0.00198 (SBP - 125.17) + 0.00672 (heart rate - 81.443) - 0.05918 (Hgb - 12.345) + 0.01424 (white blood cells - 8.344) + 0.00502 (urea nitrogen - 17.085) + 0.17451 (calcium - 9.441) - 8e-05 (platelets - 276.764) - 0.01363 (lymphocytes per 100 white blood cells in blood - 16.982) + 0.00467 (AST - 23.164) + 0.00271 (ALP - 87.966) - 0.00349 (protein - 69.592) - 0.00565 (ALT - 20.505) - 0.03886 (albumin - 38.568) - 0.00106 (bilirubin - 0.483) - 0.01571 (lymphocytes - 1.269) + 0.0089 (carbon dioxide - 26.889) - 0.02273 (chloride - 99.978) + 0.10163 (monocytes - 0.613) - 0.02502 (eosinophils per 100 white blood cells in blood - 2.252) + 0.00084 (LDH - 209.625) - 0.08805 (tumor stage - 3.5) - 0.22453 (HPV status - 0.472).

[0435] The follicle-specific RoPro2 score is given by the formula: 0.0468 (age - 66.416) + 0.70266 (gender - 0.516) + 1.06061 (number of sites of metastasis - 0.012) + 0.06024 (ECOG - 0.556) + 0.26978 (NLR - 0.493) - 0.02437 (BMI - 29.51) - 0.17142 (oxygen - 96.821) + 0.00584 (SBP - 129.754) + 0.01086 (heart rate - 78.578) - 0.05796 (Hgb - 12.858) - 0.01222 (white blood cells - 7.63) + 0.02537 (urea nitrogen - 17.532) + 0.01785 (calcium - 9.334) - 0.00083 (platelets - 227.01) - 0.00522 (lymphocytes in blood / 100 white blood cells - 24.463) + 0.01112 (AST - 22.11) + 0.00406 (ALP - 83.673) + 0.01715 (protein - 67.455) - 0.03429 (ALT - 20.377) - 0.07823 (albumin - 39.938) - 0.58047 (bilirubin - 0.566) - 0.04803 (lymphocytes - 2.253) + 0.11677 (carbon dioxide - 26.334) + 0.11495 (chloride - 102.349) + 0.42239 (monocytes - 0.602) + 0.20839 (eosinophils in blood / 100 white blood cells - 2.822) + 0.00141 (LDH - 240.005) - 0.14411 (tumor stage - 3.034).

[0436] The RoPro2 score specific to pancreatic cancer is given by the formula: 0.00638 (age - 67.378) + 0.05687 (gender - 0.541) - 0.62763 (smoking - 0.35) + 0.00541 (number of metastatic sites - 0.093) + 0.22063 (ECOG - 0.894) + 0.03629 (NLR - 0.667) - 0.00469 (BMI - 26.238) - 0.02826 (oxygen - 97.09) - 0.00141 (SBP - 126.099) + 0.00776 (heart rate - 82.714) - 0.0175 (Hgb - 11.903) + 0.00485 (white blood cells - 8.717) + 0.01408 (urea nitrogen - 15.205) + 0.10756 (calcium - 9.203) - 0.00054 (platelets - 257.947) - 0.00733 (lymphocytes in blood / 100 white blood cells - 19.318) + 0.00386 (AST - 37.701) + 0.00114 (ALP - 185.199) - 0.00548 (protein - 66.673) - 0.00654 (ALT - 37.784) - 0.04763 (albumin - 36.334) + 0.03451 (bilirubin - 0.777) - 0.10421 (lymphocytes - 1.539) + 0.00822 (carbon dioxide - 25.679) - 0.02157 (chloride - 100.587) + 0.26008 (monocytes - 0.696) - 0.00193 (eosinophils in blood / 100 white blood cells - 2.499) + 0.00048 (LDH - 259.294) + 0.00544 (tumor stage - 3.522) - 0.29951 (IsSurgery - 0.2).

[0437] All analyses were performed using the statistical analysis package (R Core Team, 2013). It will be appreciated that other packages can additionally or alternatively be employed for analysis.

[0438] The systems and methods of the above-described embodiments can be implemented in a computer system, in particular in computer hardware or computer software.

[0439] The term "computer system" includes hardware, software, and data storage devices for implementing a system or executing a method according to the above-described embodiments. For example, a computer system may include a central processing unit (CPU), an input device, an output device, and a data memory. Preferably, the computer system has a monitor to provide a visual output display (for example, in the design of a business process). The data memory may include RAM, a disk drive, or other computer-readable media. A computer system may include multiple computing devices connected via a network and capable of communicating with each other on the network.

[0440] The methods of the above embodiments may be provided as a computer program, a computer program product, or a computer-readable medium carrying the computer program. The computer program is arranged to execute the above methods when executed on a computer.

[0441] The term "computer-readable medium" includes, but is not limited to, any non-transitory medium or media that can be read and accessed directly by a computer or computer system. Such media may include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as optical disks or CD-ROMs; electronic storage media such as memory, including RAM, ROM, and flash memory; and hybrids and combinations of the foregoing, such as magnetic / optical storage media.

[0442] Unless the context dictates otherwise, the descriptions and definitions of the features above are not limited to any particular aspect or embodiment of the invention and apply equally to all aspects and embodiments described.

[0443] As used herein, "and / or" will be taken as a specific disclosure of each of two particular features or components, with or without the other. For example, "A and / or B" will be taken as a specific disclosure of each of (i) A, (ii) B, and (iii) A and B, as if each were individually listed herein.

[0444] It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the from particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. The term "about" in connection with a numerical value is optional and means, for example, + / - 10%.

[0445] Throughout this specification, including the claims that follow, unless the context requires otherwise, the words "comprise" and "comprising" and variations such as "comprises" and "comprising" will be understood to imply the inclusion of stated integers or steps or groups of integers or steps but not the exclusion of any other integers or steps or groups of integers or steps.

[0446] Other aspects and embodiments of the present invention provide the above-described aspects and embodiments wherein the term "comprising" is replaced by the term "consisting of" or "consisting essentially of, unless the context dictates otherwise.

[0447] The features disclosed in the preceding description, or in the following claims, in terms of the manner of expressing or implementing the disclosed functions in their specific forms, or in terms of the methods or processes for obtaining the disclosed results, may be used individually or in any combination in their various forms as appropriate to implement the present invention.

[0448] Although the present invention has been described in conjunction with the above exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art upon presentation of this disclosure. Therefore, the above exemplary embodiments of the present invention are intended to be illustrative rather than restrictive. Various changes may be made to the described embodiments without departing from the spirit and scope of the present invention.

[0449] For the avoidance of any doubt, any theoretical explanations provided herein are intended to improve the reader's understanding. The inventors do not wish to be bound by any of these theoretical explanations.

[0450] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0451] Table 15 provides detailed information on the 26 parameters included in Roche Prognostic Score 1 (RoPro1) and the 29 parameters included in RoPro2, parameters that can be substituted for the parameters included in RoPro1 or RoPro2, and known parameters for specific cancer types. The cancer patient information used in the methods described herein may include data corresponding to one or more parameters listed in Table 15 as disclosed elsewhere herein.

[0452]

[0453]

[0454]

[0455]

[0456]

[0457]

[0458]

[0459]

[0460]

[0461]

[0462]

[0463]

[0464]

[0465] Table 16 shows the average patient time on study BP29428 by RMHS and RoPro1 5% quantile. Also shown is the number of patients (N) in each group of low RMHS, high RMHS, and each RoPro1 quantile. Table cells show patient time (days) on study BP29428 by RMHS (group mean) and RoPro1 5% quantile classification (running group mean). For RoPro1, the numerical boundaries of the 5% quantile are explicitly given.

[0466]

[0467]

[0468] Table 17 shows the average patient time on study BP29428 for patients with a primary diagnosis of bladder cancer by RMHS and RoPro1 10% quantile. Also shown is the number of patients (N) in each group of low RMHS, high RMHS, and each RoPro1 quantile. Table cells show patient time (days) on study BP29428 by RMHS (group mean) and RoPro1 10% quantile classification (running group mean). For RoPro1, the numerical boundaries of the 10% quantile are explicitly given. Due to the smaller sample size, 10% rather than 5% quantiles are used in this table.

[0469] RMHS Study days (mean) Number of patients (N) High 78 21 Low 146 41 RoPro1 at 5th percentile Study days (running mean) Number of patients (N) >1.21 1 6 [0.80；1.21] 25 7 [0.66；0.80] 51 6 [0.50；0.66] 106 6 [0.41；0.50] 102 6 [0.28；0.41] 94 6 [0.21；0.28] 95 6 [0.07；0.21] 100 6 [-0.08；0.07] 121 6 <-0.08 123 7

[0470] Table 18 shows the average patient time on study BP29428 by RMHS and RoPro2 10th percentile for patients. Also shown is the number of patients (N) in each group of low RMHS, high RMHS, and each RoPro2 10th percentile. Table cells show patient time on study BP29428 (days) by RMHS (group mean) and RoPro2 10th percentile category (running group mean). For RoPro2, the numerical boundaries of the 10th percentile are explicitly given.

[0471]

[0472]

[0473] Table 19 shows the average patient time on study OAK by RMHS and RoPro2 10th percentile for patients. Also shown is the number of patients (N) in each group of low RMHS, high RMHS, and each RoPro2 10th percentile. Table cells show patient time on study (days) by RMHS (group mean) and RoPro2 10th percentile category (running group mean). For RoPro2, the numerical boundaries of the 10th percentile are explicitly given.

[0474] RMHS Study days (mean) Number of patients Low 372 884 High 244 303 RoPro (decile) Study days (mean) Number of patients [-1.94；-0.74] 471 105 [-0.74；-0.52] 452 91 [-0.52；-0.35] 439 111 [-0.35；-0.19] 395 143 [-0.19；-0.05] 377 139 [-0.05；0.11] 324 122 [0.11；0.29] 314 153 [0.29；0.5] 248 134 [0.5；0.81] 201 113 [0.81；2.94] 148 76

[0475] Table 20: List of Abbreviations

[0476]

[0477]

[0478] Reference

[0479] All documents mentioned in this specification are hereby incorporated by reference in their entirety.

[0480] A predictive model for aggressive non-Hodgkin’s lymphoma. N. Engl. J. Med. 329:987-94, 1993

[0481] Altman D, Simon R: Statistical aspects of prognostic factor studies in oncology. Br J Cancer 69:979-985, 1994

[0482] Banks E, Joshy G, Abhayaratna WP, et al.: Erectile dysfunction severity as a risk marker for cardiovascular disease hospitalisation and all-cause mortality: a prospective cohort study. PLoS Med 10:el001372, 2013

[0483] Charlson ME, Pompei P, Ales KL, et al.: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 373-383, 1987

[0484] Cox DR.: Regression Models and Life-Tables. Journal of the Royal Statistical Society Series B (Methodological) Vol. 34

[0485] Curtis MD, Griffith SD, Tucker M, et al.: Development and Validation of a High-Quality Composite Real-World Mortality Endpoint. Health Serv Res 53:4460-4476, 2018

[0486] Derek Grose, Graham Devereux, Louise Brown, et al.: Simple and Objective Prediction of Survival in Patients with Lung Cancer: Staging the Host Systemic Inflammatory Response. Lung Cancer International Volume 2014, Article ID 731925s.

[0487] Ganna A, Ingelsson E: 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study. The Lancet 386:533-540, 2015

[0488] Graf E, Schmoor C, Sauerbrei W, et al.: Assessment and comparison of prognostic classification schemes for survival data. Stat Med 18:2529-2545, 1999

[0489] Halabi S, Owzar K: The Importance of Identifying and Validating Prognostic Factors in Oncology. Semin Oncol 37:e9-e18, 2010

[0490] Hu FB, Willett WC, Li T, et al.: Adiposity as compared with physical activity in predicting mortality among women. N Engl J Med 351 :2694-2703, 2004

[0491] Jin J, Hu K, Zhou Y, Li W: Clinical utility of the modified Glasgow prognostic score in lung cancer: A meta-analysis. PLoS ONE 12(9):e0184412, 2017.

[0492] ​​​​​Kahn MG, Callahan TJ, Barnard J, et al.: A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMs Gener Evid Methods Improve Patient Outcomes 4: 18, 2016

[0493] Kinoshita A, Onoda H, Imai N, et al.: The Glasgow Prognostic Score, an inflammation based prognostic score, predicts survival in patients with hepatocellular carcinoma. BMC Cancer 52, 2013

[0494] McGee DL, Liao Y, Cao G, et al.: Self-reported health status and mortality in a multiethnic US cohort. Am J Epidemiol 149: 41-46, 1999

[0495] Nieder C, Dalhaug A: A new prognostic score derived from phase I study participants with advanced solid tumours is also valid in patients with brain metastasis. Anticancer Res. 977-9, 2010

[0496] R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http: / / www.R-project.org / .

[0497] Reid VL, McDonald R, Nwosu AC, Mason SR, Probert C, Ellershaw JE, Coyle S: A systematically structured review of biomarkers of dying in cancer patients in the last months of life; An exploration of the biology of dying. PLoS One 12(4), 2017

[0498] Rittmeyer A, Barlesi F, Waterkamp D, et al.: Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 389:255-265, 2017

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Claims

1. A computer-readable medium comprising instructions for causing a data storage device to execute a method for assessing the risk of death of a cancer patient, the method comprising: using the model by inputting cancer patient information into the model to generate a score indicating the risk of death for the cancer patient, and assessing the risk of death by comparing the generated score to one or more predetermined thresholds, or by comparing the generated score to scores generated for other cancer patients in the same group, The patient information includes data corresponding to each of the following parameters: (i) albumin levels in serum or plasma; (ii) Eastern Cooperative Oncology Group (ECOG) performance status, where: 0 indicates fully active; 1 indicates limited vigorous physical activity but ambulatory and able to perform light or sedentary work; 2 indicates ambulatory and able to care for themselves but unable to perform any work activities for more than 50% of waking hours; 3 indicates limited self-care and confined to bed for more than 50% of waking hours; and 4 indicates completely disabled, unable to perform any self-care, and completely confined to bed or a chair; (iii) the ratio of lymphocytes to leukocytes in the blood; (iv) smoking status, where 1 indicates smoking history and 2 indicates no smoking history; (v) age; (vi) TNM classification for the staging of malignant tumors, where: 1 represents stage I, 2 represents stage II, 3 represents stage III, 2.75 represents stage IIIa, and 3.25 represents stage IIIb. "a" changes the number to -0.25 and "b" changes the number to +0.25; (vii) heart rate; (viii) chloride or sodium levels in serum or plasma; (ix) urea nitrogen levels in serum or plasma; (x) sex, where 0 represents female and 1 represents male; (xi) hemoglobin or hematocrit levels in the blood; (xii) aspartate aminotransferase enzyme activity levels in serum or plasma; (xiii) alanine aminotransferase enzyme activity level in serum or plasma; (xiv) systolic or diastolic blood pressure; (xv) lactate dehydrogenase enzyme activity level in serum or plasma; (xvi) body mass index; (xvii) protein levels in serum or plasma; (xviii) platelet levels in the blood; (xix) number of metastatic sites; (xx) the ratio of eosinophils to white blood cells in the blood; (xxi) serum or plasma calcium levels; (xxii) oxygen saturation level in arterial blood; (xxiii) alkaline phosphatase enzyme activity level in serum or plasma; (xxiv) neutrophil-to-lymphocyte ratio (NLR) in blood; (xxv) total bilirubin levels in serum or plasma; (xxvi) white blood cell levels in the blood; (xxvii) lymphocyte levels in the blood; (xxviii) carbon dioxide levels in the blood; and (xxix) monocyte levels in the blood, in: The method further comprises forming a model by performing a multivariate cox regression analysis on training data, the training data comprising parameters (i) to (xxix) for a plurality of subjects; Forming the model includes: assigning a respective weight w to each of the respective parameters, wherein the weight is positive for harmful parameters and negative for protective parameters; and Assigning a respective mean m to each of the respective parameters for a plurality of subjects, wherein an output of the model is given by a sum of the selected parameters according to the following formula: Output = Σw(input - m); The method further comprises assessing whether the risk of death is high risk or low risk, wherein: When the comparison of the generated score with a first predetermined threshold indicates that the generated score is above the first predetermined threshold, then assessing the risk of death as a high risk, wherein the first predetermined threshold is 0, 1 or 1.05; or When the comparison of the generated score with a second predetermined threshold indicates that the generated score is below the second predetermined threshold, then assessing the risk of death as low risk, wherein the second predetermined threshold is 0, -1 or -1.19; or assessing the risk of death as high when a comparison of the generated score with the generated scores of other cancer patients in the same group indicates that the generated score is in the top 50%, top 10%, or top 5% of the generated scores of other cancer patients in the same group; or assessing the risk of death as low when a comparison of the generated score with the generated scores of other cancer patients in the same group shows that the generated score is in the bottom 50%, the bottom 10%, or the bottom 5% of the scores generated by other cancer patients in the same group; and The cancer is selected from the group consisting of melanoma, non-small cell lung cancer (NSCLC), bladder cancer, chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), hepatocellular carcinoma (HCC), metastatic breast cancer, metastatic colorectal cancer (CRC), metastatic renal cell carcinoma (RCC), multiple myeloma, ovarian cancer, small cell lung cancer (SCLC), follicular lymphoma, pancreatic cancer, and head and neck cancer, Wherein parameters (ii), (iv), (v)-(vii), (ix), (x), (xii), (xv), (xix), (xxi), (xxiii)-(xxvi) and (xxix) are harmful and parameters (i), (iii), (viii), (xi), (xiii), (xiv), (xvi)-(xviii), (xx), (xxii) and (xxvii)-(xxviii) are protective.

2. A computer-readable medium comprising instructions that cause a data storage device to execute a method for selecting cancer patients for inclusion in a clinical trial, the method comprising using the computer-readable medium according to claim 1 to assess whether the cancer patients are at a high risk of death or a low risk of death, and selecting patients assessed as being at a low risk of death to be included in the clinical trial.

3. A computer-readable medium comprising instructions that cause a data storage device to execute a method for selecting a cancer patient for treatment with an anti-cancer therapy, the method comprising using the computer-readable medium according to claim 1 to assess whether the cancer patient is at a high risk of death or a low risk of death, and selecting the cancer patient assessed as being at a low risk of death for treatment with the anti-cancer therapy.

4. A computer-readable medium comprising instructions that cause a data storage device to perform a method for monitoring a cancer patient during treatment with an anti-cancer therapy, the method comprising using the computer-readable medium of claim 1 to assess whether the cancer patient is at a high risk of death or a low risk of death, wherein cancer patients assessed as being at a low risk of death are selected to continue treatment with the anti-cancer therapy, and cancer patients assessed as being at a high risk of death are selected to discontinue treatment with the anti-cancer therapy.

5. A computer-readable medium comprising instructions that cause a data storage device to execute a method for evaluating the results of a clinical trial of an anti-cancer therapy performed on a cancer patient, the method comprising using the computer-readable medium of claim 1 to evaluate whether the cancer patient participating in the clinical trial is at a high risk of death or a low risk of death.

6. A computer-readable medium comprising instructions that cause a data storage device to execute a method for selecting a cancer patient for inclusion in a clinical trial, the method comprising using the computer-readable medium of claim 1 to identify a first cancer patient and a second cancer patient having the same risk of death, and enrolling the patients in the clinical trial.

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