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Definitions

• The“SLN gene signatures” provided herein classify an individual sed on prognosis and/or classify an individual as having a metastasis-positive or— gative sentinel lymph node (SLN).
• The“N-SLN gene signatures” provided herein ssify an individual as having a metastasis-positive or -negative non-sentinel lymph de (N-SLN). ACKGROUND OF THE INVENTION
• utaneous melanoma is a malignant tumor that arises from the pigment producingelanocytic cells located mostly in the stratum basale of the skin's epidermis. Most ions appear with signs as described by the ABCDE rules: Asymmetry, Border egularity, Color variegation, Diameter larger than 6 mm and Evolution, i.e. thendency to change rapidly Abbasi NR, Shaw HM, Rigel DS, et al. Early Diagnosis ofutaneous Melanoma Revisiting the ABCD Criteria. JAMA. 2004;292(22):2771-2776). e sites are typically asymptomatic, however they can cause itching and/or bleeding -pecially in later stages.
• the detection of suspicious lesions is usually done by self- amination of the skin, which is recommended to perform routinely on the basis of theBCDE criteria or“ugly duckling signs” (Grob J. The‘Ugly Duckling’ Sign: Identification the Common Characteristics of Nevi in an Individual as a Basis for Melanoma reening. Arch Dermatol. 1998;134:103-104).
• Grob J. The‘Ugly Duckling’ Sign Identification the Common Characteristics of Nevi in an Individual as a Basis for Melanoma reening. Arch Dermatol. 1998;134:103-104.
• melanoma is subsequently mally diagnosed by a physician, it is important to determine the specific subtype,nce there are multiple clinical and pathological varieties known.
• the most common m is cutaneous melanoma - a superficial spreading melanoma which comprises about % of cases— and which is especially frequently occurring in fair skinned people.
• the accurate discrimination between thefferent stage classes is important and is most commonly based on the TNM system,ferring to the thickness of the primary Tumor (T), the presence and/or extent of tumor ls to the lymph Nodes (N) and the presence of distant Metastasis to other organs (M).
• T the thickness of the primary Tumor
• N the presence and/or extent of tumor ls to the lymph Nodes
• M distant Metastasis to other organs
• physicians take into account the tumor thickness but also additional characteristics such as presence of ulceration and the mitotic rate of the primary tumor cells.
• prognosis refers to a prediction of the medical outcome of the patient.
• an individual may be classified as having a poor prognosis or a good prognosis.
• the prognosis of a patient afflicted with melanoma indicates, e.g., likelihood of long-term survival, overall survival, progression free survival, prediction of relapse versus disease remission, and disease progression.
• the surgical technique has been improved by the implementation of a dual-modality, intraoperative approach using blue dye and a radiotracer with gamma probe detection.
• the pathological assessment was upgraded by the employment of serial sectioning of the SLN and immunohistochemistry. This has resulted in the better identification of the draining first lymph node or group of nodes that is situated in close proximity of the tumor (i.e., the SLN) and is therefore a likely site of metastatic disease. This procedure is also referred to as“sentinel lymph node mapping”.
• resection is understood to mean surgical removal of malignant tissue characteristic of melanoma from a human patient. According to one embodiment, resection shall be understood to mean removal of malignant tissue such that the presence of remaining malignant tissue within said patient is undetectable with available methods.
• the rate at which the SLNBs are classified as positive are highly variable and depending to a great extent on the known prognostic factors of the primary tumor.
• the percentage of SLN metastasis is 15-30%, whereas in thin melanomas it is shown to be 5.2%.
• the current edition of the Melanoma Expert Panel staging guidelines noted the clinical relevance of the subcategorization of Tl melanomas at 0.8 mm. This is based on the detected trend in several survival studies of Tl melanomas that there is a potential clinical intercept in the region of 0.7 to 0.8 mm.
• the long term follow up of patients after a SLNB it was shown that regional nodal recurrence takes place in patients with sentinel nodes that were initially tumor-free.
• SLNB is not only a method to potentially stage cutaneous melanoma, but is also part of a treatment, which depending on the metastatic classification of the SLN, may or may not be necessary.
• the SLNB procedure can cause complications for a patient and is costly. Therefore, this procedure is only performed on a select group of patients that are considered to have a higher risk for metastatic spread - from the vast majority of low risk lesions.
• the risk for metastasis may be assessed by the evaluation of
• SLN biopsies are not recommended for Tla thin melanomas,‘may be recommended’ for a Tib thin-melanoma patients, are recommended for T2 and T3 intermediate thickness melanoma patients and‘may be recommended’ for T4 thick- melanoma patients.
• a further object of the present invention is to predict the prognosis of an individual afflicted with primary cutaneous melanoma. This information is useful, e.g., in order to determine an optimal treatment strategy.
• the invention provides a method for classifying an individual afflicted with primary cutaneous melanoma, comprising determining in a sample from said individual a gene expression signature, wherein the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, SPP1, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, GDF15 and IL8, more preferably wherein the gene expression signature comprises ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBR1, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• a gene signature comprising three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• Also provided is method for determining a treatment and/or diagnostic work-up schedule for an individual afflicted with cutaneous melanoma comprising determining in a sample from said individual the level of expression of three or more of the following genes: ITGB3, PLAT, SPP1, GDF15 and IL8 and determining a treatment and/or diagnostic work-up schedule based on the expression levels.
• the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, GDF15 and IL8, more preferably wherein the gene expression signature comprises ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALS1 and TGFBRl.
• a gene signature comprising three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, PRKCB, SERPINE2, ADAM 12, LGALS1 and TGFBRl
• Methods are also provided for predicting the prognosis of an individual afflicted with primary cutaneous melanoma, comprising determining in a sample from said individual a gene expression signature, wherein the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, SPP1, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• a gene signature comprising three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl
• the individual is classified as having a metastasis-positive SLN or is classified as having a metastasis-negative SLN.
• the prognosis of the individual is determined based on the gene expression levels.
• an individual is classified as having a poor prognosis or a good prognosis.
• the individual can be selected for SLNB based on said classification and/or expression levels.
• the individual classified as having a metastasis-positive SLN or rather a poor prognosis is treated by performing a SLNB and/or adjuvant treatment.
• the invention also provides a method for classifying an individual afflicted with primary cutaneous melanoma, comprising determining in a sample from said individual a gene expression signature, wherein the gene expression signature comprises at least one of the following genes: KRT14, SPP1, FN1, and LOXL3.
• the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, SPP1, GDF15 and IL8,
• the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, GDF15 and IL8, more preferably wherein the gene expression signature comprises ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBR1, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• a gene signature comprising three or more of the following genes: GDF15, MLANA, PLAT, IL
• the invention also provides a method for treating an individual afflicted with primary cutaneous melanoma, comprising
• the gene expression signature comprises at least one of the following genes KRT14, SPPl, FN1, and LOXL3,
• RNA transcripts - amplifying the cDNAs to produce amplicons from the cDNAs for determination of expression levels of the RNA transcripts.
• the invention also provides a method for analyzing a gene signature in an individual afflicted with primary cutaneous melanoma, said method comprising
• RNA transcripts - amplifying the cDNAs to produce amplicons from the cDNAs for determination of expression levels of the RNA transcripts.
• kit for use in classifying an individual afflicted with primary cutaneous melanoma comprising primer pairs for amplifying:
• kit comprising primer pairs for amplifying three or more of the following genes: ITGB3, PLAT, SPPl, GDF15 and IL8, more preferably the kit comprising primer pairs for amplifying ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• a gene signature comprising three or more of the following genes: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• Figure 1 Average ROC curves for the logistic regression classifiers trained in DLCV on: 1) ITGB3, PLAT, SPP1, GDF15 and IL8 gene signature (molecular model), 2) clinic- pathological variables (age and Breslow depth), 3) ITGB3, PLAT, SPP1, GDF15 and IL8 gene signature and clinic-pathological variables combined.
• the x-axis represents the false positive discovery rate (i.e. 1- specificity), the y-axis the true discovery rate (i.e. sensitivity).
• Figure 2 ROC curves for the ITLP score and for the SLN gene signature (referred to as the“logistic regression model”), on the entire 770 patient cohort.
• the x-axis represents the false positive discovery rate (i.e. 1- specificity), the y-axis the true discovery rate (i.e. sensitivity).
• Figure 3 Boxplots of the area under the ROC curves for the different ITGB3, PLAT, SPP1, GDF15 and IL8 gene subsets, for the full set of 5 ITGB3, PLAT, SPP1, GDF15 and IL8 genes and for the ITLP signature.
• Figure 4 Average ROC curves for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic- pathological variables combined.
• the x-axis represents the false positive discovery rate (i.e. 1- specificity), the y-axis the true discovery rate (i.e. sensitivity).
• Figure 5 Boxplots of the area under the ROC curves for the different gene subsets and for the full set of 4 genes.
• Figure 6 OVERALL PERFORMANCE COMPARISON: CL VS GE VS GECL.
• Figure 7 NPV vs SLNBRR. Negative Predictive Value (NPV) versus the Sentinel Lymph Node reduction Rate (SLNB RR) for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic-pathological variables combined.
• Figure 8 GENE SUBSETS- AUC BOXPLOTS. Boxplots of the Area Under the Curve (AUC) of the ROC curve for logistic regression classifiers with subsets of 2, 3, 4, 5, 6, 7, 8 genes trained on the entire cohort.
• AUC Area Under the Curve
• the disclosure provides, in part, methods, kits, gene signatures and means of detecting such gene signatures to perform an analysis of a primary cutaneous melanoma tumor tissue sample.
• the disclosure provides an“SLN gene signature”.
• the SLN gene signature classifies an individual afflicted with primary cutaneous melanoma, in particular, the gene signature classifies the risk of the individual having a metastasis - positive SLN and/or a poor prognosis. This risk assessment is useful for physicians and patients when deciding whether an SLNB procedure and/or an alternative treatment strategy is warranted. This assessment is also useful when selecting patients for inclusion in clinical trials.
• the SLN is the first lymph node (or set of first lymph nodes) to receive lymphatic drainage from a tumor and is the first lymph node (or set of first lymph nodes) where cancer is likely to spread.
• a N-SLN is a lymph node that is not the first lymph node to receive lymphatic drainage from a tumor. Such N-SLN is often a node in the same nodal basin or in close proximity to the SLN.
• the gene signature classifies the risk of metastasis-positive SLN.
• the methods disclosed herein classify an individual as having a metastasis-positive SLN or having a metastasis-negative SLN.
• the gene signatures classifies the prognosis of the individual.
• prognosis refers to the prediction of a medical outcome and can be based on measures such as overall survival, melanoma specific survival, recurrence free survival, relapse free survival and distant relapse free survival.
• SLN gene signature can reduce the number of surgical procedures for patients classified as metastasis-negative SLN (and/or classified as having a good prognosis).
• patients having intermediate lesions would likely have undergone an SLNB procedure with a good chance that the SLN was, in fact, metastasis negative.
• Accurate classification of such patients with the SLN gene signature avoids the need for an SLNB procedure and can be used to replace the SLNB as the current standard of care for intermediate lesions.
• the reduction of unnecessary SLNBs reduces the overall health care costs and reduces the number of patients suffering from complications caused by the removal of SLNs.
• the SLN gene signature can reduce the number of surgical procedures for patients classified as metastasis-negative SLN (and/or classified as having a good prognosis).
• patients having intermediate lesions would likely have undergone an SLNB procedure with a good chance that the SLN was, in fact, metastasis negative.
• SLN metastasis positive or negative also provides prognostic information and can be used to for determining a treatment or diagnostic work-up schedule.
• the SLN gene signature is able to more accurately predict prognosis than the standard of care SLN biopsies (see Example 7). While not wishing to be bound by theory, one possible explanation as to the improved prognostic power of the gene signature over SLN biopsies relates to the technical limitation of performing such biopsies (e.g., identifying the correct lymph node to biopsy, limitations of tumor cell detection, human error in processing/classifying samples).
• the disclosed gene signature may be able to predict SLN metastasis at a stage before it can be detected in a biopsy (e.g., the tumor has metastasized and tumor cells are in route to the SLN).
• the SLN signature can be used to replace the SLNB as a criterion for inclusion in clinical trials and/or additional treatment.
• a further advantage of the SLN gene signature is that it can identify patients with thin thickness melanoma that in current standard of care may not be eligible for SLNB based on clinical parameters, but based on the gene signature are high risk for metastasis positive SLN.
• the gene signature will greatly increase the identification of metastasis-positive SLN in thin ( ⁇ 0.8mm) melanoma patients, that are currently not eligible for SLNB procedures according to the guidelines. The early detection and treatment of such patients will increase progression free and overall survival for this patient sub-population.
• a gene expression signature (i.e., the SLN gene signature) comprising one or more of the following genes: ITGB3, PLAT,
• the disclosure provides a gene signature comprising one or more, preferably two or more, more preferably three or more of the following genes: ITGB3, PLAT, SPP1, GDF15 and IL8.
• Suitable gene signatures include the following combinations: ITGB3 and PLAT; ITGB3 and SPPl; ITGB3 and GDF15; ITGB3 and IL8; PLAT and SPP1; PLAT and GDF15; PLAT and IL8; SPPl and GDF15; SPPl and IL8; GDF15 and IL8.
• the gene signature comprises ITGB3, PLAT, and one or more of SPPl, GDF15 and IL8.
• the SLN gene signature comprises three or more of the following genes: ITGB3, PLAT, SPPl, GDF15 and IL8.
• the SLN gene signature comprises four or more of the following genes: ITGB3, PLAT, SPPl, GDF15 and IL8.
• the SLN gene signature comprises all the following genes: ITGB3, PLAT, SPPl, GDF15 and IL8.
• the gene expression signature comprises three or more of the following genes: ITGB3, PLAT, GDF15 and IL8, more preferably wherein the gene expression signature comprises ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4,
• SERPINE2, and TGFBR1 more preferably wherein the gene expression signature comprises GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBR1, more preferably wherein the gene expression signature comprises GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBR1.
• the gene signature comprises at least three, at least four, or at least five of the following: ITGB3, PLAT, GDF15, SPP1 and IL8.
• the gene signature comprises ITGB3, PLAT, GDFlo, and IL8.
• the gene signature comprises at least three, at least four, at least five, at least six, at least seven, at least eight or all of the following: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl.
• the inventors demonstrate that a gene signature lacking ADIPOQ performs similarly. Therefore, in some embodiments the gene signature comprises at least three, at least four, at least five, at least six, at least seven, or all of the following: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBRl.
• the gene signature comprises:
• the gene signature comprises 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 11, or all of the following: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBRl.
• the gene signature comprises at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or all of the following: GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, PRKCB, SERPINE2, ADAM 12, LGALSl and TGFBRl.
• the gene signature consists of the above mentioned genes.
• methods for performing an analysis may comprise measuring the expression of additional genes (e.g., for normalization) but only the gene signature is used to classify an individual.
• the ITGB3 gene encodes Integrin beta-3.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_000212.2 (17 June 2018).
• the PLAT gene encodes plasminogen activator, tissue type.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_001319189.1 (1 July 2018).
• the SPP1 gene encodes secreted phosphoprotein 1.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_001040058.1 (24 June 2018).
• the GDF15 gene encodes growth differentiation factor 15.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_004864.3 (17 June 2018).
• the IL8 gene encodes interleukin 8.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under AF043337.1 (1 February 2001).
• the MLANA gene encodes melan-A.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_005511 (20 Oct 2018).
• the LOXL4 gene encode lysyl oxidase like 4.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_032211 (22 Nov 2018).
• the ADIPOQ gene encodes adiponectin, ClQ and collagen domain containing.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_004797 (2 Dec 2018).
• the PRKCB gene encodes protein kinase C beta.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_212535 (12 Nov 2018).
• the SERPINE2 gene encodes serpin family E member 2.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_006216 (17 Nov 2018).
• the ADAM12 gene encodes ADAM metallopeptidase domain 12.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_003474 (5 Aug 2018).
• LGALSl gene encodes galectin 1.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_002305 (22 Nov 2018).
• the TGFBRl gene encodes transforming growth factor beta receptor 1.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_004612 (28 Oct 2018).
• the disclosure further provides methods of classifying an individual comprising determining in a sample the SLN gene signature.
• the individual may be classified as having metastasis-positive SLN or metastasis-negative SLN.
• the individual may be classified as having good or poor prognosis.
• a gene signature associated with SLN metastasis was previously reported (Meves et al.
• the algorithm uses the clinicopathological variables age, Breslow depth and ulceration combined with primary melanoma gene expression of four genes ITGB3, LAMB1, PLAT and TP53, to predict SLN metastasis.
• the present SLN gene signature outperforms the previously reported signature.
• the disclosure provides an“N-SLN gene signature”.
• the N-SLN gene signature classifies an individual afflicted with primary cutaneous melanoma, in particular, the gene signature classifies the risk of the individual having a metastasis- positive non-sentinel lymph node (N-SLN). This risk assessment is useful for physicians and patients when deciding treatment options and to determine the patient’s prognosis.
• the N-SLN gene signature classifies the risk of metastasis positive N-SLN.
• An individual may be classified as having metastasis -positive N-SLN or metastasis-negative N-SLN.
• the invasion of tumor cells to distantly located lymph nodes is an indicator of poor prognosis and suggests the use of more aggressive forms of treatment. Early detection and treatment is expected to improve patient outcome.
• a gene expression signature (i.e., the N- SLN gene signature) comprising one or more of the following genes: KRT14, SPP1, FNl, LOXL3, is useful to classify individuals and in particular to determine risk of
• the disclosure provides a gene signature comprising at least one of the following genes: KET14, SPP1, FNl, LOXL3.
• the gene signature comprises at least two or at least three of the following genes: KRT14, SPP1, FNl, LOXL3.
• the N-SLN gene signature comprises or consists of KRT14, SPP1, FNl, LOXL3.
• the gene signature consists of the above mentioned genes.
• methods for performing an analysis may comprise measuring the expression of additional genes (e.g., for normalization) but only the gene signature is used to classify an individual.
• the N-SLN gene signature is determined in an individual who is suffering from a recurrence/relapse of cutaneous melanoma and/or has already received an SLN biopsy.
• the KRT14 gene encodes keratin 14.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_000526.4 (17 June 2018).
• the FNl gene encodes fibronectin 1.
• An exemplary Homo sapiens mRNA sequence can be found at the NCBI database under NM_001306129.1 (3 June 2018).
• the LOXL3 gene encodes lysyl oxidase like 3.
• An exemplary homo sapiens mRNA sequence can be found at the NCBI database under NM_001289165.1 (30 June 2018).
• the disclosure further provides methods of classifying an individual comprising determining in a sample the N-SLN gene signature.
• the individual may be classified as having metastasis-positive N-SLN or metastasis-negative N-SLN.
• methods are provided which determine both the SLN gene signature and the N-SLN gene signature.
• Analysis of the gene signatures disclosed herein may be performed in any individual, including mammals and humans, although humans are preferred.
• the individual has been diagnosed with a cutaneous melanoma of T1-T3.
• the individual has not yet undergone a biopsy of the SLN of the primary elanoma, in particular when the gene signature is the SLN gene signature.
• the genegnature is particularly useful to classify individuals with a young age, with a high totic rate (e.g., above 2/mm 2 ), previous history of disease, familial history of disease th poor outcome, and/or with lymph vascular invasion.
• e gene expression signatures are useful for predicting the risk or likelihood that tumor ls have metastasized to an SLN or N-SLN.
• theassification of an individual refers to a probability or“risk of” and not that 100% of all tients that are predicted to be at risk will in fact have detectable metastases (referred as sensitivity or Positive Percent Agreement) nor that 0% of all patients that are edicted not to have metastases will in fact be clear of metastases (referred to as ecificity or Negative Percent Agreement).
• sensitivity or Positive Percent Agreement a probability or“risk of” and not that 100% of all tients that are predicted to be at risk will in fact have detectable metastases
• ecificity or Negative Percent Agreement referred to as ecificity or Negative Percent Agreement.
• the SLN and SLN gene expression signatures exhibit high performance levels for both sensitivity d specificity.
• the SLN gene signature is able to better edict the prognosis of an individual afflicted with melanoma than the standard of careLN biopsy.
• metastasis refers the presence of tumor cell clusters and does not include lymph nodes which onlyntain isolated or rare tumor cells.
• metastasis refers to the esence of cell clusters that are at least 0.1mm in diameter either with or without tra-capsular extension. is within the purview of one of skill in the art to obtain a suitable sample for termining gene expression. Suitable samples include primary cutaneous melanoma ion biopsies.
• Such biopsies include a resected lesion (e.g., wide-excision removal of a mor).
• Samples may be processes or preserved by any means known in the art to be mpatible with gene expression profiting.
• the sample may be a formalin ed paraffin embedded primary cutaneous melanoma lesion biopsy, as well as a frozen mple.
• the sample is an RNA-containing sample.
• General methods for mRNA traction are well known in the art and are disclosed in standard textbooks of molecularology, including Ausubel et al. (1997) Current Protocols of Molecular Biology, John ley and Sons.
• RNA isolation can be performed using rification kit, buffer set and protease from commercial manufacturers, such as Qiagen, cording to the manufacturer’s instructions (QIAGEN Inc., Valencia, Calif.). For ample, total RNA from cells in culture can be isolated using Qiagen RNeasy mini- columns. Numerous RNA isolation kits are commercially available and can be used in the methods of the invention.
• the methods disclosed herein comprise determining a gene expression signature.
• the methods comprise determining a level of gene expression.
• Gene expression levels can be determined by measuring the level of nucleic acid or protein expression.
• the level of mRNA expression is determined.
• nucleic acid or protein is purified from the sample and gene expression is measured by nucleic acid or protein expression analysis.
• the level of protein expression can be determined by any method known in the art including ELISAs,
• nucleic acid expression levels are determined.
• the level of nucleic acid expression may be determined by any method known in the art including RT-PCR, quantitative PCR, Northern blotting, gene sequencing, in particular RNA sequencing, and gene expression profiling techniques.
• Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).
• the nucleic acid is RNA, such as mRNA or pre-mRNA.
• the level of RNA expression determined may be detected directly or it may be determined indirectly, for example, by first generating cDNA and/or by amplifying the RNA/cDNA.
• a primary melanoma sample is obtained; RNA is extracted from the tissue sample; followed by reverse transcribing an RNA transcript of the genes of interest (e.g., biomarkers and housekeeping genes) to produce cDNAs of the RNA transcripts; and amplifying the cDNAs to produce amplicons from the cDNAs for determination of expression levels of the RNA transcripts.
• gene expression may be determined by NanoString gene expression analysis.
• NanoString is a multiplexed method for detecting gene expression and provides a method for direct measurement of mRNAs without the use of
• NanoString and aspects thereof are described in Geiss et al., "Direct multiplexed measurement of gene expression with color- coded probe pairs" Nature Biotechnology 26, 317 - 325 (2008);
• the level of expression need not be an absolute value but may rather be a normalized expression value or a relative value.
• the levels of expression can be normalized against housekeeping or reference gene expression.
• genes include ABCF1, ACTB, ALASl, CLTC, G6PD, GAPDH, GUSB, HPRTl, LDHA, PGK1, POLR1B, POLR2A, RPL19, RPLPO, SDHA, TBP, and TUBB. Normalization is also useful when expression is determined based on microarray data. Normalization allows for correction for variation within microarrays and across samples so that data from different chips can be simultaneously analyzed. The robust multi-array analysis (RMA) algorithm may be used to pre-process probe set data into gene expression levels for all samples.
• RMA multi-array analysis
• the expression levels are determined using Real-time PCR (i.e., quantitative PCR or qPCR).
• qPCR real-time PCR
• reactions are characterized by the point in time during cycling when amplification of a target is first detected rather than the amount of target accumulated after a fixed number of cycles. This point when the signal is first detected is referred to as the threshold cycle (Ct).
• expression of the gene signature is quantified relative to each other by normalization against the expression of housekeeping genes by subtracting the Ct of the signature genes from the averaged Ct of the housekeeping genes.
• these ACt values are then combined with the patient’s age and melanoma lesion’s Breslow Depth in an algorithm to calculate the prediction of SLN metastasis.
• the housekeeping genes used for normalization are ACTB, RPLP0 and RPL8. However, other housekeeping genes may be used.
• the ratios of the gene expression signals may be subsequently combined with clinical variables in an algorithm to calculate the prediction of the outcome of a patient SLNB. Results are expressed as a binary classification (negative or positive).
• A“negative” result would indicate that the individual has a low risk of metastasis-positive SLN or rather that the individual has a good prognosis, whereas a“positive” result would indicate that the individual has a high risk of a metastasis-positive SLN or rather a poor prognosis.
• the differential expression of one or more genes of the signature in an individual indicates that the individual is at risk of metastasis, or rather, indicates the prognosis of the individual.
• differ entially- expressed means that the measured expression level in a subject differs significantly from a reference.
• the reference may be a single value or a numerical range. It is within the purview of a skilled person to determine the appropriate reference value.
• the reference value is a predetermined value.
• the reference value is the average of the expression value in a particular patient class.
• the reference value may be the average of the expression value in a class of patients that have clinically confirmed SLN metastasis (or for the N-SLN signature, patients that have clinically confirmed N-SLN metastasis).
• a reference value may also be in the form of or derived from an equation. It is within the purview of one skilled in the art to determine whether the expression level in the patient differs“significantly” from a reference.
• the reference value is determined from a cohort of melanoma patients who underwent an SLNB as described in the examples. It is clear to a skilled person that data from similar studies may also be used.
• the strength of the correlation between the expression level of a differentially-expressed gene and a specific patient response class may be determined by a statistical test of significance. For example, a chi square test may be used to assign a chi square value to each differentially-expressed marker, indicating the strength of the correlation of the expression of that marker to a specific patient response class. Similarly, the T-statistics metric and the Wilkins' metric both provide a value or score indicative of the strength of the correlation between the expression of the marker and its specific patient response class. In addition, SAM or PAM analysis tools may be used to determine the strength of correlations.
• the gene expression signature from an individual is compared to the reference expression signature to determine whether the gene expression signature from an individual is sufficiently similar to the reference profile.
• the gene expression signature from an individual is compared to a plurality of reference expression signatures to select the reference expression profile that is most similar to the gene expression profile from an individual. Any method known in the art for comparing two or more data sets to detect similarity between them may be used to compare the gene expression signature from an individual to the reference expression profiles.
• classification refers to identifying to which set of categories a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
• An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.
• Many classifiers are known in the art, with linear or non-linear classifier boundaries, such as but not limited to: ClaNC, nearest mean classifier, weighted voting method, simple Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Support Vector Machines (SVM), or the k-nearest neighbor (k-nn) classifier.
• LDA linear discriminant analysis
• QDA quadratic discriminant analysis
• SVM Support Vector Machines
• k-nn k-nearest neighbor
• Sensitivity refers to the proportion of actual positives that are correctly identified as such and high sensitivity is desired to avoid false negatives (e.g., patients classified as metastasis negative that are in fact positive).
• Specificity refers to the proportion of actual negatives that are correctly identified as such and high specificity is desired to avoid false positives (e.g., patients classified as metastasis positive that are in fact negative).
• the classifiers are trained for high sensitivity in order to identify individuals with metastasis.
• the methods for classifying an individual further utilize the age of the individual and/or the Breslow depth of the tumor.
• the ulceration and/or the mitotic rate may be determined.
• Breslow depth is measured from the top of the granular layer of the epidermis (or, if the surface is ulcerated, from the base of the ulcer) to the deepest invasive cell across the broad base of the tumor (dermal/subcutaneous). Ulceration refers to the sloughing of dead tissue and is thought to reflect rapid tumor growth, leading to the death of cells in the center of the melanoma.
• the mitotic rate may be measured by examining the excised tumor and counting the number of cells exhibiting mitosis.
• a combined model which comprises the gene signature comprising GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4, SERPINE2 and TGFBRl and the clinical variables of age and Breslow depth.
• the gene signature additionally comprises AIDPOQ.
• kits for determining the gene expression signatures disclosed herein comprise primer pairs for performing qPCR on the gene signatures disclosed herein.
• the kits comprise primer pairs for performing qPCR on two or more, preferably three or more, of the following genes: ITGB3, PLAT, SPPl, GDF15 and IL8; and/or one or more of the following genes: KRT14, SPPl, FN1, LOXL3.
• the kits comprise primer pairs for housekeeping genes, such as ACTB, RPLP0 and RPL8.
• the kits further comprising one or more of the following: DNA
• the kit comprises primer pairs for amplifying three or more of the following genes: ITGB3, PLAT, GDF15 and IL8, more preferably the kit comprising primer pairs for amplifying ITGB3, PLAT, GDF15 and IL8.
• the gene expression signature comprises GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, SERPINE2, and TGFBRl, more preferably wherein the gene expression signature comprises GDF15, MLANA, PLAT,
• kits comprise a control nucleic acid for one or more, preferably for each, primer pair.
• the control nucleic acid is cDNA and more preferably the cDNA corresponds to a sequence that spans at least one intron/exon boundary of the respective gene. Such cDNA is useful to distinguish gene expression from genomic contamination.
• one or more primers of the primer pair are chemically modified.
• Such modified primers include fluorescently or radioactively labeled primers.
• the results of the gene expression analyzes disclosed herein are useful for determining a diagnostic work-up schedule. For example, an individual classified as have metastasis positive SLN or a poor prognosis can undergo a SLNB.
• immunotherapy is administered to an individual that is predicted to be SLN positive or rather predicted to have poor prognosis.
• a subsequent SLNB readout can be a measure of response to the immunotherapy.
• an appropriate treatment regime can be determined.
• treatment refers to reversing, alleviating, delaying the onset of, or inhibiting the progress of melanoma, or one or more symptoms thereof.
• individuals classified as having metastatic-positive SLN or rather a poor prognosis may be treated with SLNB.
• the location of the SLN may be determined based on the location of the melanoma and/or with methods such as“SLN mapping”, as known to a skilled person and described herein.
• a metastasis-positive N-SLN can be treated by performing a surgical procedure, for instance a surgical lymph node dissection.
• Metastatic-N-SLN may be treated with a complete lymph node dissection and/or other therapies for treating melanoma .
• a cancer treatment is administered to the individual.
• an“adjuvant treatment” is administered to an individual.
• Adjuvant treatment refers to the administration of one or more drugs to a patient after surgical resection of one or more cancerous tumors, where all detectable and resectable disease (e.g. cancer) has been removed from the patient, but where there remains a statistical risk of relapse.
• Adjuvant treatment is useful to diminish the likelihood or the severity of reoccurrence or the disease.
• melanoma therapies which may be indicated based on the gene expression signatures include:
• Chemotherapy e.g., dacarbazine (DTIC), temozolomide (Temodal), carboplatin
• Targeted therapy drugs e.g., BRAF inhibitors (vemurafenib (Zelboraf) and dabrafenib (Tafinlar)) and MEK inhibitors (cobimetinib (Cotellic) and trametinib (Mekinist));
• cytokines e.g., Interferon alfa-2b or Interleukin-2
• immune checkpoint inhibitors e.g., Ipilimumab (Yervoy), Nivolumab (Opdivo), Pembrolizumab (Keytruda)
• oncolytic immunotherapy e.g., cytokines (e.g., Interferon alfa-2b or Interleukin-2)
• immune checkpoint inhibitors e.g., Ipilimumab (Yervoy), Nivolumab (Opdivo), Pembrolizumab (Keytruda)
• oncolytic immunotherapy e.g., cytokines (e.g., Interferon alfa-2b or Interleukin-2)
• immune checkpoint inhibitors e.g., Ipilimumab (Yervoy), Nivolumab (Opdivo), Pembrolizumab (Keytruda)
• oncolytic immunotherapy e
• Suitable routes include oral, rectal, nasal, topical (including buccal and sublingual), vaginal, and parenteral (including subcutaneous, intramuscular, intraveneous, intradermal, intrathecal, and epidural).
• parenteral including subcutaneous, intramuscular, intraveneous, intradermal, intrathecal, and epidural.
• verb“to consist” may be replaced by“to consist essentially of’ meaning that a compound or adjunct compound as defined herein may comprise additional component(s) than the ones specifically identified, said additional component(s) not altering the unique characteristic of the invention.
• the word“approximately” or“about” when used in association with a numerical value preferably means that the value may be the given value of 10 more or less 1% of the value.
• Example 1 Gene signature to predict SEN metastasis status
• the load of the metastasis as measured by the volume of metastatic disease can differ significantly:
• contingency tables In order to evaluate the performance of a classifier, contingency tables have been constructed. From these contingency tables two criteria have been derived, namely the PPA (Positive Percent Agreement) and NPA (Negative Percent Agreement): which are defined as:
• Classifier Logistic regression with penalized maximum likelihood
• the double loop cross validation method can be described in a few steps:
• the data is split (stratified) into 3 parts (different splits for each repeat).
• Tablet depicts the performance of the of the final classifier trained on the entire 770 patient cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training. If the coefficient is positive, then higher value implies increased risk. If the coefficient is negative, then the reduced value implies decreased risk. Variables with larger (absolute) coefficients have a larger contribution.
• Table 2 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 3 The parameter“Age” is entered in years and the“Breslow depth” in millimetres. Ulceration is a Boolean variable (yes/no).
• the table depicts the performance of the of the final classifier trained on the entire 770 patient cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 4 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 5 depicts the performance of the of the final classifier trained on the entire 770 patient cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 6 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Figure 1 depicts the ROC curves for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic- pathological variables combined.
• Table 7 depicts the average performance of the classifiers trained in DLCV on: 1) gene expression (“GE”; i.e., ITGB3, PLAT, SPPl, GDF15 and IL8 gene signature, 2) clinic- pathological variables (“CL”; i.e., age and Breslow depth), 3) gene expression and clinic- pathological variables combined (“GECL”).
• GE gene expression
• CL clinic- pathological variables
• NPV97 NPV set to 0.97 in training.
• Table 8 depicts the performance of the of the ITLP score on the entire 770 patient cohort. Classifier based on ITLP score and Clinical Variables
• Table 9 depicts the performance of the of the final classifier trained on the entire 770 patient cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 10 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• ITLP vs. ITGB3, PLAT, GDF15, SPP1 and IL8 gene signature
• Figure 2 depicts the ROC curves for the ITLP score and for the ITGB3, PLAT, GDF15, SPP1 and IL8 gene signature (“referred to as logistic regression” in the figure).
• the ITGB3, PLAT, GDF15, SPP1 and IL8 gene signature clearly outperforms the ITLP signature.
• the previous examples used 5 genes: ITGB3, PLAT, GDF15, SPP1 and IL8 for the gene signature.
• ITGB3, PLAT, GDF15, SPP1 and IL8 was investigated the performance of all possible subsets of 2, 3 and 4 genes.
• the number of subsets of a specific dimension that can be selected from the total number of genes for the following signatures is as follows: 10 subsets from signatures with two genes, 10 subsets from signatures with three genes, 5 subsets from signatures with four genes and one signature comprising all 5.
• the AUC (or range thereof) was 0.68 for ITLP, 0.72-0.75 for all subsets of 2, 0.74-0.77 for all subsets of 3, 0.76-0.77 for all subsets of 4, and 0.77 for the 5 gene signature. This is also shown in Figure 3. Accordingly, all gene signatures comprising at least two of the following genes: ITGB3, PLAT, GDF15, SPP1 and IL8 outperform the ITLP signature.
• Example 5 Performance for the 43 samples with low volume of metastatic disease Patients with low volume of metastatic disease (volume 1 & 2) have been excluded, in first instance, from the cohort used for training the classifier. It remains controversial whether samples having cell clusters less than 0.1 mm in diameter should be considered metastatic positive and from a clinical viewpoint are generally considered negative. In the present study, 43 patients were initially excluded from the analysis since they have a volume 1 or 2. Applying the ITGB3, PLAT, GDF15, SPP1 and IL8 classifier on such patients, resulted in the classification of 29 as positive and 14 as negative.
• Example 6 Misclassification analysis
• misclassified positive samples are mostly from patients with thin melanomas (less than 2 mm), without ulceration, and with no angio-lymphatic invasion. In other words, these are patients that present a very low a priori risk of developing metastasis. There are a few sample misclassified in all the 100 repetitions of the algorithm.
• misclassified negative samples are mostly from patients with thick melanomas (more than 2 mm), with ulceration, and with angio-lymphatic invasion. In other words, these are patients that present a high a priori risk of developing metastasis. There are a few sample misclassified in all the 100 repetitions of the algorithm.
• the distribution of the predicted probability is uni-modal not Gaussian with a long right tale.
• the threshold used to choose the operating point falls nearby the mean of the distribution.
• the estimated probabilities do not exceed 0.6.
• Example 7 Prognostic association of the SLN gene signature
• Kaplan-Meier survival estimates for three types of survival were generated for the SLN classifier comprising the genes GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl, and TGFBRl (referred to in the example as“GECL”) (Tables 19-21), SLNB status (Tables 19-21), and the combination of those two (Table 22-24).
• Tables 19-26(b) describes the results with an NPV set to 0.97 in training
• Tables 19-26(c) describes the results with an NPV set to 0.98 in training. See example 8 for further discussion on this classifier.
• Kaplan-Meier method is used in order to estimate survival probability at several time intervals.
• the log-rank test is a nonparametric test used in comparing survival curves between two or more groups.
• the hazard ratio (HR) has been defined as the ratio of (risk of outcome in one
• Hazard ratio of 1 means lack of association, a hazard ratio greater than 1 suggests an increased risk, and hazard ratio below 1 suggests a smaller risk. Hazard ratio is used to represent the relative difference between only two groups.
• Example 8 Refinement of SEN classifier
• the parameters of the logistic classifier model based on clinical-pathological variables are as follows.
• Table 27 depicts the performance of the final classifier trained on the entire 754 patient cohort for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) NPV98, NPV set to 0.98 in training. If the coefficient is positive, then higher value implies increased risk. If the coefficient is negative, then the reduced value implies decreased risk. Variables with larger (absolute) coefficients have a larger contribution.
• Table 28 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) NPV98, NPV set to 0.98 in training.
• Table 29 depicts the performance of the final classifier trained on the entire 754 patient cohort for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) NPV98, NPV set to 0.98 in training. If the coefficient is positive, then higher value implies increased risk. If the coefficient is negative, then the reduced value implies decreased risk. Variables with larger (absolute) coefficients have a larger contribution.
• Table 30 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2)
• Table 31 depicts the performance of the final classifier trained on the entire 754 patient cohort for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) NPV98, NPV set to 0.98 in training. If the coefficient is positive, then higher value implies increased risk. If the coefficient is negative, then the reduced value implies decreased risk. Variables with larger (absolute) coefficients have a larger contribution.
• Table 32 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) NPV98, NPV set to 0.98 in training.
• Figure 6 depicts the ROC curves for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic- pathological variables combined and
• Figure 7 depicts the Negative Predictive Value (NPV) versus the Sentinel Lymph Node reduction Rate (SLNB RR) for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic -pathological variables combined.
• NPV Negative Predictive Value
• SLNB RR Sentinel Lymph Node reduction Rate
• Table 33 depicts the average performance of the classifiers trained in DLCV on: 1) gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ
• GE gene expression
• MLANA MLANA
• PLAT PLAT
• IL8ITGB3,LOXL4 IL8,ITGB3,LOXL4
• C clinic- pathological variables
• GECL gene expression and clinic-pathological variables combined
• GECL i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ, SERPINE2 and TGFBRl.
• max bACC balanced accuracy
• Table 34 depicts the average performance of the classifiers trained in DLCV on: 1) gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4,ADIPOQ,
• PRKCB,SERPINE2,ADAM12,LGALS1 and TGFBRl gene signature 2) clinic- pathological variables (“CL”; i.e., age, Breslow depth and presence of
• GECL i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ ,SERPINE2 and TGFBRl.
• SEeqSP specificity
• Table 35 depicts the average performance of the classifiers trained in DLCV on: 1) gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, PRKCB, SERPINE2, ADAM12, LGALSl and TGFBR1 gene signature, 2) clinic-pathological variables (“CL”; i.e., age, Breslow depth and presence of angiolymphatic invasion), 3) gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBR1). For an operating points with NPV set to 0.97 in training (NPV97).
• Table 36 depicts the average performance of the classifiers trained in DLCV on: 1) gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ
• GE gene expression
• MLANA MLANA
• PLAT PLAT
• IL8ITGB3,LOXL4 IL8,ITGB3,LOXL4
• C clinic- pathological variables
• angiolymphatic_invasion 3) gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBR1).
• GECL i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBR1
• Table 37 depicts the average performance stratified by T stage of the classifiers trained in DLCV on clinic-pathological variables (“CL”; i.e., age, Breslow depth and presence of angiolymp h atic_m vasion) .
• CL clinic-pathological variables
• NPV97 0.97 in training
• Table 38 depicts the average performance stratified by T stage of the classifiers trained in DLCV on gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,
• Table 39 depicts the average performance stratified by T stage of the classifiers trained in DLCV on gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBRl). For an operating points with NPV set to 0.97 in training (NPV97).
• GECL i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBRl.
• Table 40 depicts the average performance stratified by T stage of the classifiers trained in DLCV on clinic-pathological variables (“CL”; i.e., age, Breslow depth and presence of angiolymphatic_mvasion). For an operating point with NPV set to 0.98 in training (NPV98).
• CL clinic-pathological variables
• NPV98 For an operating point with NPV set to 0.98 in training (NPV98).
• Table 41 depicts the average performance stratified by T stage of the classifiers trained in DLCV on gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ , PRKCB, SERPINE2, AD AM12, LGALSl and TGFBRl.
• GE gene expression
• NPV98 For an operating point with NPV set to 0.98 in training (NPV98).
• Table 42 depicts the average performance stratified by T stage of the classifiers trained in DLCV on gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 , ADIPOQ ,SERPINE2 and TGFBRl). For an operating points with NPV set to 0.98 in training (NPV98). Performance stratified by clinical staging
• Table 43 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on clinic-pathological variables (“CL”; i.e., age, Breslow depth and presence of angiolymphatic_invasion). For an operating point with NPV set to 0.97 in training (NPV97).
• CL clinic-pathological variables
• NPV97 For an operating point with NPV set to 0.97 in training (NPV97).
• Table 44 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on gene expression (“GE”; i.e., GDF15, MLANA, PLAT, IL8,ITGB3, LOXL4 ,ADIPOQ ,PRKCB,SERPINE2,ADAM12,LGALSl and TGFBR1.
• GE gene expression
• NPV97 For an operating point with NPV set to 0.97 in training (NPV97).
• Table 45 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ ,SERPINE2 and TGFBR1). For an operating points with NPV set to 0.97 in training (NPV97).
• GECL i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ ,SERPINE2 and TGFBR1
• Table 46 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on clinic-pathological variables (“CL”; i.e., age, Breslow depth and presence of angiolymphatic_invasion). For an operating point with NPV set to 0.98 in training (NPV98).
• CL clinic-pathological variables
• Table 47 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on gene expression (“GE”; i.e., GDF15, MLANA, PLAT,
• IL8,ITGB3,LOXL4 ADIPOQ ,PRKCB,SERPINE2,ADAM12,LGALS1 and TGFBR1.
• NPV98 0.98 in training
• Table 48 depicts the average performance stratified by clinical stage of the classifiers trained in DLCV on gene expression and clinic-pathological variables combined (“GECL”: i.e. age, Breslow depth, GDF15, MLANA, PLAT, IL8,ITGB3,LOXL4 ,ADIPOQ,
• Figure 8 depicts the boxplots of the Area Under the Curve (AUC) of the ROC curve for logistic regression classifiers with subsets of 2, 3, 4, 5, 6, 7, 8 genes selected from GDF15, MLANA, PLAT, IL8, ITGB3, LOXL4, ADIPOQ, SERPINE2, and TGFBR1 and trained on the entire cohort.
• AUC Area Under the Curve
• Table 49 depicts the Number of subsets of a specific dimension that can be selected from the total number of genes in each the signature, and the performance in terms of minimum and maximum area under the ROC curve.
• Example 9 Non-sentinel lymph-node (N-SLN) profiler.
• Completion lymph node dissection with the removal of Non-Sentinel Lymph Node (N-SLN), has been standard for clinically node-negative melanoma patients with positive sentinel lymph nodes (SLN).
• SLN biopsy followed by immediate CLND improves regional disease control, and randomized clinical trials have shown that early surgery for lower volume SLN-positive disease results in fewer long-term sequelae (e.g. lymphedema) than surgery at nodal relapse.
• SLN and N-SLN metastasis is an adverse prognostic factor used to select patients for adjuvant therapy.
• the number of patients in the entire cohort was 140.
• Table 11 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 12 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 13 depicts the performance of the final classifier trained on the entire 140 patients cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 14 depicts the performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 15 depicts performance of the of the final classifier trained on the entire 140 patients cohort classifier for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Table 16 depicts performance of the classifiers trained in DLCV, averaged over 100 repeats, for four different operating points: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training, 4) LRNn025, log likelihood ratio for the negative test result set to 0.25 in training.
• Figure 4 depicts average ROC curves for the logistic regression classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic-pathological variables combined.
• the x-axis represents the false positive discovery rate (i.e. 1- specificity), the y-axis the true discovery rate (i.e. sensitivity).
• Figure 5 Boxplots of the area under the ROC curves for the different gene subsets and for the full set of 4 genes.
• Table 17 depicts average performance of the classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinic-pathological variables combined. Three different operating points have been considered: 1) max bACC: max balanced accuracy, 2) SEeqSP, sensitivity equal to specificity, 3) NPV97, NPV set to 0.97 in training.
• the N-SLNprofiler profiler signature comprises 4 genes: KRT14, SPPl, FNl and LOXL3. We have looked as well at all possible subsets of 2, 3 and 4 genes
• AUC Area Under the Curve
• Figure 5 depicts boxplots of the area under the ROC curves for the different gene subsets and for the full set of 4 genes.
• N-SLNprofiler can be used to select which patients should undergo the CLND procedure.
• the performance of the classifier based on gene expression is of interest, since at the moment (i) there are no methods available to select the patients that would benefit from the CLND procedure, and (ii) the clinicopathologic variables used by the classifier might not be always available in the clinic.
• Table 18a depicts an average performance of the classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinicopathologic variables combined.
• the chosen operating point is NPV97, namely, NPV set to 0.97 in training.
• Table 18b depicts an average performance of the classifiers trained in DLCV on: 1) gene expression, 2) clinic-pathological variables, 3) gene expression and clinicopathologic variables combined. The chosen operating point has been selected such that it maximizes the balanced accuracy.
• the performance for Breslow depth >2 though inferior to the performance for Breslow depth ⁇ -2 mm is still acceptable since the classifier achieves a NPV of 90% in a sub- population with a prior probability of SLNB positivity of 35%: upon taking the test and having a negative outcome, the probability of having a positive SLN drops from 35% to 10%.
• Fictitious data (see Table) is used as an example for the classification method, using 2 genes for simplicity, to predict whether a sample will be labeled as lymph node positive or lymph node negative by the classifier (the method/model is the same for both SLN profiler and for N-SLN profiler, just the parameters and the identities of the genes and clinical variables are different).
• the table describes the model parameters b 0 , /3 ⁇ 4, b 2 for a two genes (x,y) toy model, the fictitious gene expression data AC t , the estimated log odd- ratio log the estimated probability p, and the estimated output class labels based on a cutoff Q— 0.19.
• the log odd-ratio and probability is calculated based on equation 1, and the probability is calculated using equation 2.
• the output label is assigned by comparing the estimated probability to the cutoff Q: if the estimated probability is greater than or equal to Q , then the sample is classified as node positive; if the estimated probability is smaller than Q then the sample is classified as node negative
• SLNB.RR SLNB Reduction Rate
• Table 25a Hazard Ratios and p-values for the 2 curves from the GECL classifier outputs and SLNB biopsy results.
• Table 26a Multivariate Hazard Ratios and p-values for the 2 curves from the GECL classifier outputs and SLNB biopsy

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