Patent Application
20210381062
Lung cancer is the deadliest form of cancer in the United States and the world. An estimated 221,000 new lung cancer diagnoses are expected in the United States in 2015, and approximately 158,000 men and women are expected to fall victim to the disease during the same time period. The high mortality rate is due, in part, to a failure in 70% of patients to detect lung cancer when it is localized and surgical resection remains feasible.
In 2011, the National Lung Screening Trial (NLST) demonstrated that annual screening of high-risk smokers by low-dose chest CT (LDCT) could lead to the detection of earlier stage lung cancers and reduce mortality by 20%. The expectation is that, similar to other cancers for which there are established screening programs (e.g., breast, prostate and colon cancers), regular lung cancer screening could lead to lung cancer becoming considerably less deadly. As a result, Medicare is now paying for lung cancer screening in defined high risk cohorts. In the NLST trial, there was, however, a considerable false-positive rate associated with CT screening (greater than 95%), with the overwhelming majority of nodules ultimately determined to be benign.
Together, these findings have led to the development of guidelines under which additional diagnostic procedures should be performed in patients with screen-detected nodules, including those established by the Fleischner Society which recommends repeat imaging studies or invasive testing depending on the size of the lesion. Unfortunately, the diagnostic performance under these guidelines remains low and often results in a delay in the diagnosis of early stage lung cancer and unnecessary invasive procedures for those without disease.
With more than 9 million people in the United States meeting NLST screening eligibility criteria, there is a critical need for more accurate, non-invasive tools to prioritize patients for repeat imaging or invasive procedures following the detection of nodules by screening LDCT. Also needed are additional criteria for lung cancer screening eligibility. The current guidelines for determining screening eligibility are based on age and smoking history and present two fundamental challenges. First, even though these guidelines suggest the screening of almost 3% of the total United States population, they capture less than 30% of the cases of lung cancer that are diagnosed each year. Second, the prevalence of lung cancer among the screen-eligible cohort is only about 1%, indicating that the burden of screening could be greatly reduced if screening could be more accurately targeted. Taken together, these data suggest that there is a tremendous need and an opportunity to improve screening eligibility beyond age and smoking history.
Disclosed herein are assays and methods of diagnosing lung cancer and methods of identifying subjects at risk for developing lung cancer. The inventions disclosed herein provide non-invasive, or in certain embodiments minimally-invasive, methods for diagnosing lung cancer based in-whole or in-part on analysis of gene expression in nasal epithelial cells. Accordingly, provided herein are non-invasive and minimally invasive methods for the diagnosis, prognosis, monitoring and/or follow up of progression or success of treatment based upon the differential expression of certain genes in nasal epithelial cells (e.g., one or more of the 535 genes identified in Table 12 or Table 21).
In certain embodiments, disclosed herein are methods of diagnosing lung cancer in a subject, such methods comprising the steps of: (a) measuring a biological sample comprising nasal epithelial cells of the subject for expression of one or more genes (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes); and (b) comparing the expression of the one or more genes to a control sample of those genes taken from individuals without cancer; wherein the one or more genes are selected from the group consisting of genes in Tables 12, 13 or 21, and wherein differential expression of the subject's one or more genes relative to the control sample is indicative of the subject having lung cancer. In some aspects, non-differential expression of the subject's one or more genes relative to the control sample is indicative of the subject not having lung cancer.
Also disclosed herein are methods of diagnosing lung cancer in a subject, such methods comprising the steps of: (a) measuring a biological sample comprising nasal epithelial cells of the subject for expression of one or more genes (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes); and (b) comparing the expression of the one or more genes to a control sample of those genes from individuals with cancer; wherein the one or more genes are selected from the group consisting of genes in Tables 12 or 13, and wherein differential expression of the subject's one or more genes relative to the control sample is indicative of the subject not having lung cancer. In certain aspects, non-differential expression of the subject's one or more genes relative to the control sample is indicative of the subject having lung cancer.
In certain aspects, the inventions disclosed herein relate to methods of determining whether a subject has quit smoking comprising the steps of: (a) measuring a biological sample comprising nasal epithelial cells of the subject for expression of one or more genes selected from the group consisting of genes in Tables 5 or 6 (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes); and (b) comparing the expression of the one or more genes to a control sample of those genes from non-smokers; wherein altered expression of the subject's genes relative to the control sample is indicative of the subject having quit smoking. In certain aspects, non-altered expression of the subject's one or more genes relative to the control sample is indicative of the subject not having quit smoking.
In still other embodiments, also disclosed herein are methods of determining whether a subject has quit smoking, such methods comprising the steps of: (a) measuring a biological sample comprising nasal epithelial cells of the subject for expression of one or more genes selected from the group consisting of genes in Tables 5 or 6 (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes); and (b) comparing the expression of the one or more genes to a control sample of those genes obtained from smokers; wherein altered expression of the subject's genes relative to the control sample is indicative of the subject not having quit smoking. In some aspects, non-altered expression of the subject's one or more genes relative to the control sample is indicative of the subject having quit smoking.
In certain aspects, the present inventions also relate to methods of determining the likelihood that a subject has lung cancer, such methods comprising: (a) subjecting a biological sample comprising the subject's nasal epithelial cells to a gene expression analysis, wherein the gene expression analysis comprises comparing gene expression levels of one or more genes (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes) selected from the group of genes identified in Tables 12 or 13 to the expression levels of a control sample of those genes from individuals without cancer; and (b) determining the likelihood that the subject has lung cancer by determining differential expression of the subject's one or more genes relative to the group of genes in Tables 12 or 13, wherein differential expression of the subject's genes relative to the control sample is indicative of the subject having a high likelihood of lung cancer. In some embodiments, non-differential expression of the subject's one or more genes relative to the control sample is indicative of the subject having a low likelihood of lung cancer.
In certain embodiments, the one or more genes comprise one or more of the leading edge genes identified in Table 21. For example, any of the methods disclosed herein may comprise, consist of or consist essentially of determining the differential expression of at least one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more of the leading edge genes identified in Table 21. In some aspects, the methods disclosed herein comprise determining the differential expression of all of the leading edge genes identified in Table 21.
In certain aspects, the inventions disclosed herein are directed to methods of determining the likelihood that a subject has lung cancer, such methods comprising: (a) subjecting a biological sample comprising the subject's nasal epithelial cells to a gene expression analysis, wherein the gene expression analysis comprises comparing gene expression levels of one or more genes (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes) selected from the group of genes in Tables 12 or 13 to the expression levels of a control sample of those genes from individuals with cancer; and (b) determining the likelihood that the subject has lung cancer by determining differential expression of the subject's one or more genes relative to the group of genes in Tables 12 or 13, wherein differential expression of the subject's genes relative to the control sample is indicative of the subject having a low likelihood of lung cancer. In some embodiments, non-differential expression of the subject's one or more genes relative to the control sample is indicative of the subject having a high likelihood of lung cancer.
In any of the embodiments disclosed herein, at least about two genes are measured (e.g., at least two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty, sixty, seventy, eighty, ninety, one hundred or more genes are measure). In some embodiments, at least about five genes are measured. In some embodiments, at least about ten genes are measured. In some embodiments, at least about twenty genes are measured. In still other embodiments, at least about thirty genes are measured. In yet other embodiments, at least about forty genes are measured. In still other embodiments, at least about fifty genes are measured.
In some embodiments, the 535 genes listed in Table 12 or Table 21 are grouped into one or more of the four clusters of related genes identified. For example, in some aspects, the genes measured comprise one or more of those genes identified in cluster 1 of Table 12. In some aspects, the genes measured comprise one or more of those genes identified in cluster 2 of Table 12. In some aspects, the genes measured comprise one or more of those genes identified in cluster 3 of Table 12. In some aspects, the genes measured comprise those genes identified in cluster 4 of Table 12. In yet another embodiment, the genes measured comprise at least one gene (e.g., one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty five, thirty, forty, fifty or more genes) from each of clusters 1, 2, 3 and 4 of Table 12.
In certain embodiments, the methods and assays disclosed herein are used in combination with one or more clinical risk factors (e.g., the subject's smoking status) for determining a subject's risk of having lung cancer or at risk of developing lung cancer. For example, such methods and assays may be combined with one or more clinical risk factors selected from the group consisting of advanced age, smoking status, the presence of a lung nodule greater than 3 cm on CT scan, the location of the lesion or nodule (e.g., centrally located, peripherally located or both) and the amount of time since the subject quit smoking. Combining any of the methods and assays disclosed herein with, for example, a subject's positive smoking status may be more indicative of the subject having lung cancer and thereby enhance the predictive value and/or sensitivity of the methods and assays disclosed herein. Similarly, in some embodiments, the combination of the methods and assays disclosed herein and a subject's age (e.g., advanced age) may also be indicative of the subject having, or of being at increased risk of having lung cancer. In still other embodiments, the methods and assays disclosed herein comprise performing or reviewing the results of one or more imaging studies (e.g., chest X-ray, assessing the subject for the presence of a lung nodule or lesion greater than 3 cm on the subject's CT scan, assessing lesion or nodule location), which if positive, may be further indicative of the subject having lung cancer. In some embodiments, the methods and assays disclosed herein may further comprise a step of assessing the subject's time since quitting smoking, which if greater than 15 years may be indicative of the subject having lung cancer.
In certain aspects of any of the methods, compositions or assays disclosed herein, the one or more genes assessed comprise, consist of, or consist essentially of one or more genes from Table 14. In some embodiments of any of the methods, compositions or assays disclosed herein, the one or more genes comprise, consist of, or consist essentially of one or more genes from Table 15. In some embodiments of any of the methods compositions or assays disclosed herein, the one or more genes further comprise, consist of, or consist essentially of one or more genes from Table 13. In some embodiments, the one or more genes comprise, consist of, or consist essentially of all of the genes from Table 14. In some embodiments, the one or more genes comprise, consist of, or consist essentially of one or more genes from Table 13. In certain aspects, the one or more genes further comprise one or more genes from Table 5. In some other embodiments, the one or more genes further comprise one or more genes from Table 6.
In certain embodiments of any of the methods disclosed herein, the one or more genes (e.g., one or more genes from Table 12 or Table 21) are associated with DNA damage. In certain embodiments of any of the methods disclosed herein, the one or more genes (e.g., one or more genes from Table 12 or Table 21) are associated with regulation of apoptosis. In still other embodiments of any of the methods disclosed herein, the one or more genes (e.g., one or more genes from Table 12 or Table 21) are associated with immune system activation (e.g., one or more genes is associated with the interferon-gamma signaling pathway or associated with antigen presentation).
In some embodiments, expression of the one or more genes from the biological sample (e.g., a biological sample comprising nasal epithelial cells) is determined using a quantitative reverse transcription polymerase chain reaction, a bead-based nucleic acid detection assay or a oligonucleotide array assay.
In certain aspects of any of the methods disclosed herein, the method further comprises applying a gene filter to the expression to exclude specimens potentially contaminated with inflammatory cells.
In some embodiments, the methods and assays disclosed herein are useful for identifying subjects having, or of being at increased risk of having lung cancer. In certain aspects, the lung cancer is selected from the group consisting of adenocarcinoma, squamous cell carcinoma, small cell cancer or non-small cell cancer.
As discussed above, in some aspects, the assays and methods disclosed herein rely in part on determining the differential expression of one or more genes in a subject's nasal epithelial cells (e.g., one or more of the genes set forth in Table 12 or Table 21). In some embodiments, the one or more genes comprise DNA. In some embodiments, the one or more genes comprise RNA. In some embodiments, the one or more genes comprise mRNA.
In some embodiments, the biological sample obtained from the subject comprises nasal epithelial cells. In some embodiments, the biological sample consists or consists essentially of nasal epithelial cells. In some embodiments, the biological sample does not comprise bronchial epithelial cells or bronchial epithelial tissue. In still other embodiments, the biological sample does not comprise cells or tissues from the bronchial airway.
In certain aspects of any of the inventions disclosed herein, if such method is indicative of the subject having lung cancer or of being at risk of developing lung cancer, the method further comprises treating the subject. Accordingly, in certain embodiments, any of the methods disclosed herein may further comprise a step of administering a cancer treatment to the subject (e.g., a treatment comprising one or more of chemotherapy, radiation therapy, immunotherapy, surgical intervention and combinations thereof). For example, in those embodiments where the methods and assays disclosed herein are indicative of a subject being at a higher risk of having or developing lung cancer, the subject may be subjected to a direct tissue sampling or biopsy of the nodule, under the presumption that the positive test indicates a higher likelihood of the nodule is a cancer. Conversely, in those instances where the methods and assays disclosed herein are indicative of a subject having a reduced risk of developing lung cancer, then the subject may be subjected to further imaging surveillance (e.g., a repeat computerized tomography scan to monitor whether the nodule grows or changes in appearance before doing a more invasive procedure), or a determination made to withhold a particular treatment (e.g., chemotherapy) on the basis of the subject's favorable or reduced risk of having or developing lung cancer.
Similarly, in certain aspects of any of the inventions disclosed herein, if such method is indicative of the subject having not quit smoking or of being a smoker, the method further comprises treating the subject. Accordingly, in certain embodiments, any of the methods disclosed herein may further comprise a step of administering a smoking-cessation treatment to the subject (e.g., a treatment comprising nicotine replacement therapy).
Also disclosed herein are minimally-invasive methods and assays useful for determining the likelihood that a subject does (or does not) have lung cancer, such methods and assays comprising a step of (a) detecting, by quantitative reverse transcription polymerase chain reaction, a bead-based nucleic acid detection assay or a oligonucleotide array assay, mRNA or cDNA expression levels in a sample comprising nasal epithelial cells from a subject; (b) determining mRNA or cDNA expression levels in the sample of nasal epithelial cells of two or more gene selected from the group consisting of the genes in Table 12, Table 13 or Table 21; and (c) based on the expression levels determined in step (b) (e.g., differentially expressed levels), determining a lung cancer risk-score that is indicative of the likelihood that the subject does not haves lung cancer. In certain aspects, the subject has undergone an indeterminate or non-diagnostic bronchoscopy procedure. In certain embodiments, the genes comprise at least 1 gene from Table 13 (e.g., about one, two, three, four, five, six, seven, eight, nine or ten genes from Table 13). In some embodiments, the genes comprise at least 10 genes from Table 13 (e.g., about ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or twenty genes from Table 13). In still other embodiments, the genes comprise at least 20 genes from Table 13 (e.g., about twenty one, twenty two, twenty three, twenty four, twenty five, twenty six, twenty seven, twenty eight, twenty nine or thirty genes from Table 13). In still other aspects, the genes comprise all of the genes from Table 13.
In certain embodiments, the methods and assays disclosed herein further comprise a step of determining one or more of the subject's clinical risk factors affecting the subject's risk for having lung cancer (e.g., one or more clinical risk factors selected from the group consisting of advanced age, smoking status, the presence of a lung nodule greater than 3 cm on CT scan, lesion location and time since quitting smoking). In some embodiments, the subject's positive smoking status is indicative of the subject having lung cancer. In some aspects, the subject's advanced age is indicative of the subject having lung cancer. In some embodiments, the presence of a lung nodule greater than 3 cm on the subject's CT scan is indicative of the subject having lung cancer. In still other embodiments, the subject's time since quitting smoking greater than 15 years is indicative of the subject having lung cancer.
Also disclosed herein are compositions (e.g., diagnostic kits) and assays that comprise one or more nucleic acid probes, wherein each of the one or more nucleic acids probes specifically hybridizes with the expression products of five or more genes selected from the group of genes identified in any of Table 5, Table 6, Table 12, Table 13, Table 14, Table 15 or Table 21. In certain aspects, such one or more expression products comprise mRNA. In some aspects, such compositions measure expression of at least ten genes. In some aspects, such compositions measure expression of at least fifteen genes. In some aspects, such compositions measure expression of at least twenty genes. In some aspects, such compositions measure expression of at least thirty genes. In some embodiments, such compositions measure expression of at least forty genes. In still other embodiments, such compositions measure expression of at least fifty genes. In some embodiments, such compositions measure expression of at least one hundred genes.
In certain embodiments, the compositions (e.g., diagnostic kits) disclosed herein measure expression of those genes identified in cluster 1 of Table 12. In certain embodiments, the compositions disclosed herein measure expression of those genes identified in cluster 2 of Table 12. In yet other embodiments, the compositions disclosed herein measure expression of those genes identified in cluster 3 of Table 12. In still other embodiments, the compositions disclosed herein measure expression of those genes identified in cluster 4 of Table 12. In certain aspects, such compositions measure expression of one or more genes in Table 12 and comprise at least one gene from each of clusters 1-4.
In certain aspects of any of the methods, assays or compositions disclosed herein, the one or more genes are associated with DNA damage. In certain aspects of any of the methods, assays or compositions disclosed herein, the one or more genes are associated with the regulation of apoptosis. In certain embodiments of any of the methods, assays or compositions disclosed herein, the one or more genes are immune system activation (e.g., associated with the interferon-gamma signaling pathway and/or antigen presentation).
The above discussed, and many other features and attendant advantages of the present inventions will become better understood by reference to the following detailed description of the invention.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
Disclosed herein are novel, non-invasive or minimally invasive assays and related methods that are useful for diagnosing lung cancer or determining a subject's previous smoking status, such assays and methods comprising a step of determining the expression of one or more genes in nasal epithelial cells of a subject. For example, in certain aspects the methods disclosed herein comprise a step of comparing the expression of one or more of the 535 genes set forth in Table 12 or Table 21 in a subject's nasal epithelial cells to expression of the same genes in a nasal epithelial cell from a control subject. In certain aspects, any of the methods disclosed herein further comprise applying a gene filter to the expression to exclude specimens potentially contaminated with inflammatory cells.
The assays and methods disclosed herein provide the first ever claim of a nasal epithelium gene expression classifier composed of the specific genes described herein and that can be used to predict the presence or absence of lung cancer (e.g., adenocarcinoma, squamous cell carcinoma, small cell cancer or non-small cell cancer). Additionally, the assays and methods disclosed herein provide the first ever claim of a nasal epithelium gene expression classifier that can predict whether a subject is a current or former smoker. The assays and methods provided herein, whether used alone or in combination with other methods, provide useful information for health care providers to assist them in making early diagnostic and therapeutic decisions for a subject, thereby improving the likelihood that the subject's disease may be effectively treated. In some embodiments, methods and assays disclosed herein are employed in instances where other methods have failed to provide useful information regarding the lung cancer status of a subject.
Previous work from our group has demonstrated that gene expression in normal appearing bronchial and nasal epithelial cells is dramatically altered in current and former smokers (Zhang, et al.) and that several of these alterations persist for decades upon smoking cessation (Beane, et al.). The present inventors have extended these observations to show that gene expression in normal-appearing airway cells is also altered by smoking-related lung diseases such as COPD and lung cancer. For lung cancer, the present inventors measured gene expression in bronchial epithelial samples collected from a cohort of patients undergoing bronchoscopy for clinical suspicion of lung cancer and identified a panel of 80 genes that were indicative of the presence of lung cancer (Spira, et al., 2007) and which were independent of other clinical factors as a predictor of lung cancer (Beane, et al., 2008). More recently, a 232 gene signature was identified as differentially expressed in the bronchial epithelium of patients with lung cancer (Whitney, et al., 2015). This signature was ultimately used to develop a 23-gene bronchial genomic classifier (Whitney, et al., 2015; Silvestri, et al., 2015) that was prospectively validated in two independent cohorts consisting of over 600 patients.
The present inventions are based upon the surprising finding of a strong concordance between bronchial and nasal epithelium's response to cigarette smoke exposure, and our observation that lung disease alters gene expression in normal appearing nasal epithelium that is physically distant from the site of disease. The assays and methods disclosed herein are characterized by the accuracy with which they can discriminate lung cancer from non-lung cancer and their non-invasive or minimally-invasive nature. In some aspects, the assays and methods disclosed herein are based on detecting differential expression of one or more genes in nasal epithelial cells and such assays and methods are based on the discovery that such differential expression in nasal epithelial cells are useful for diagnosing cancer in the distant lung tissue. Accordingly, the inventions disclosed herein provide a substantially less invasive method for diagnosis, prognosis and follow-up of lung cancer using gene expression analysis of biological samples comprising nasal epithelial cells.
In contrast to conventional invasive methods, such as bronchoscopy, the assays and methods disclosed herein rely on expression of certain genes in a biological sample obtained from a subject. As the phrase is used herein, “biological sample” means any sample taken or derived from a subject comprising one or more nasal epithelial cells. As used herein, the phrase “obtaining a biological sample” refers to any process for directly or indirectly acquiring a biological sample from a subject. For example, a biological sample may be obtained (e.g., at a point-of-care facility, a physician's office, a hospital) by procuring a tissue or fluid sample from a subject. Alternatively, a biological sample may be obtained by receiving the sample (e.g., at a laboratory facility) from one or more persons who procured the sample directly from the subject.
Such biological samples comprising nasal epithelial cells may be obtained from a subject (e.g., a subject at risk for lung cancer) using a brush or a swab. The biological samples comprising nasal epithelial cells may be collected by any means known to one skilled in the art and, in certain embodiments, is obtained non-invasively. For example, in certain embodiments, a biological sample comprising nasal epithelial cells may be collected from a subject by nasal brushing. Similarly, nasal epithelial cells may be collected by brushing the inferior turbinate and/or the adjacent lateral nasal wall. For example, following local anesthesia with 2% lidocaine solution, a CYROBRUSH® (MedScand Medical, Malmδ Sweden) or a similar device, is inserted into the nare of the subject, for example the right nare, and under the inferior turbinate using a nasal speculum for visualization. The brush is turned (e.g., turned 1, 2, 3, 4, 5 times or more) to collect the nasal epithelial cells, which may then be subjected to analysis in accordance with the assays and methods disclosed herein.
In certain embodiments, the biological sample does not include or comprise bronchial airway epithelial cells. For example, in certain embodiments, the biological sample does not include epithelial cells from the mainstem bronchus. In certain aspects, the biological sample does not include cells or tissue collected from bronchoscopy. In some embodiments, the biological sample does not include cells or tissue isolated from a pulmonary lesion.
In certain embodiments, the subject has undergone an indeterminate or non-diagnostic bronchoscopy. In some embodiments, the method comprises determining that the subject does not have lung cancer based on the expression levels of one or more (such as, e.g., 2 or more) of the 535 genes set forth in Table 12 or Table 21 in a subject's nasal epithelial cells. In particular embodiments, the method comprises determining that the subject does not have lung cancer based on the expression levels in a nasal epithelial cell sample from the subject of one or more (such as, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 26, 28, 29 or 30) genes listed in Table 13. In particular embodiments, the method comprises determining the subject does or does not have cancer by applying a classifier algorithm that is trained to differentiate cancer versus non-cancer based upon the expression of at least the 30 genes expressed in Table 13. In some such embodiments, the classifier is as shown in Table 17.
To isolate nucleic acids from the biological sample, the epithelial cells can be placed immediately into a solution that prevents nucleic acids from degradation. For example, if the nasal epithelial cells are collected using the CYTOBRUSH, and one wishes to isolate RNA, the brush is placed immediately into an RNA stabilizer solution, such as RNALATER®, AMBION®, Inc. One can also isolate DNA. After brushing, the device can be placed in a buffer, such as phosphate buffered saline (PBS) for DNA isolation.
The nucleic acids (e.g., mRNA) are then subjected to gene expression analysis. Preferably, the nucleic acids are isolated and purified. However, if techniques such as microfluidic devices are used, cells may be placed into such device as whole cells without substantial purification. In one embodiment, nasal epithelial cell gene expression is analyzed using gene/transcript groups and methods of using the expression profile of these gene/transcript groups in diagnosis and prognosis of lung diseases. In some embodiments, differential expression of the one or more genes determined with reference to the one or more of the 535 genes set forth in Table 12 or Table 21.
As used herein, the term “differential expression” refers to any qualitative or quantitative differences in expression of the gene or differences in the expressed gene product (e.g., mRNA) in the nasal epithelial cells of the subject. A differentially expressed gene may qualitatively have its expression altered, including an activation or inactivation, in, for example, the presence of absence of cancer and, by comparing such expression in nasal epithelial cell to the expression in a control sample in accordance with the methods and assays disclosed herein, the presence or absence of lung cancer may be determined.
In some embodiments, subjecting the nucleic acids to gene expression analysis may comprise directly measuring RNA (e.g., mRNA expression levels). In some embodiments, subjecting the nucleic acids to gene expression analysis may comprise detecting cDNAs produced from RNA expressed in the test sample, wherein, optionally, the cDNA is amplified from a plurality of cDNA transcripts prior to the detecting step. In some embodiments, subjecting the nucleic acids to gene expression analysis comprises labeling one or more of the nucleic acids.
In certain embodiments, the methods and assays disclosed herein are characterized as being much less invasive relative to, for example, bronchoscopy. The methods provided herein not only significantly increase the sensitivity or diagnostic accuracy of lung cancer or smoking status, but also make the analysis much less invasive and thus much easier for the subjects and clinician to perform. In some embodiments, the likelihood that the subject has lung cancer is also determined based on the presence or absence of one or more clinical risk factors or diagnostic indicia of lung cancer, such as the results of imaging studies. When the assays and methods of the present invention are combined with, for example, one or more relevant clinical risk factors (e.g., a subject's smoking history), the diagnosis of lung cancer may be dramatically enhanced, enabling the detection of lung cancer at an earlier stage, and by providing far fewer false negatives and/or false positives. As used herein, the term “clinical risk factors” refers broadly to any diagnostic indicia (e.g., subjective or objective diagnostic criteria) that would be relevant for determining a subject's risk of having or developing lung cancer. Exemplary clinical risk factors that may be used in combination with the methods or assays disclosed herein include, for example, imaging studies (e.g., chest X-ray, CT scan, etc.), the subject's smoking status or smoking history and/or the subject's age. In certain aspects, when such clinical risk factors are combined with the methods and assays disclosed herein, the predictive power of such methods and assays may be further enhanced.
In some embodiments, the biological sample comprising the subject's nasal epithelial cells are analyzed for the expression of certain genes or gene transcripts, either individually or in groups or subsets. In one embodiment, the inventions disclosed herein provide a group of genes (e.g., one or more of the genes listed in Table 12, Table 13 or Table 21) that may be analyzed to determine the presence or absence of lung cancer (e.g., adenocarcinoma, squamous cell carcinoma, small cell cancer and/or non-small cell cancer) from a biological sample comprising the subject's nasal epithelial cells. In one embodiment, the inventions disclosed herein provide a group of genes (e.g., Tables 5 or 6) that may be analyzed to determine a subject's smoking status from a biological sample comprising the subject's nasal epithelial cells. For example, the biological sample may be analyzed to determine the expression of one or more genes listed in any of Table 5, Table 6, Table 12, Table 13, Table 14, Table 15 and/or Table 21, to thereby determine whether the subject has or is at risk of developing lung cancer. In certain embodiments, the nasal epithelial cells are analyzed using at least one and no more than 535 of the genes listed in Table 12 or Table 21. For example, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10-15, 15-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, or at least 10, at least 20, at least 30, at least 40 at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least or at maximum of 170, at least or at maximum of 180, at least or at maximum of 190, at least or at maximum of 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 375, 380, 390, 400, 410, 420, 425, 450, 475, 500, 525 or at least 530 or at maximum of the 535 genes as listed on Table 12 or Table 21.
One example of the gene transcript groups useful in the diagnostic/prognostic assays and methods of the invention are set forth in Table 5, Table 6, Table 12, Table 13 or Table 21. The present inventors have determined that taking any group that has at least about 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more of the Table 12 or Table 21 genes provides a much greater lung cancer diagnostic capability than chance alone. Similarly, the present inventors have determined that taking any group that has at least about 5, 10, 15, 20, 25, 30, 40, 50, 60 or more of the Tables 5 or 6 genes provides a much greater capability to determine a subject's smoking status than chance alone. Preferably one would analyze the nasal epithelial cells using more than about 20 of these genes, for example about 20-100 and any combination between, for example, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. In some instances, the present inventors have determined that one can enhance the sensitivity or diagnostic accuracy of the methods and assays disclosed herein by adding additional genes to any of these specific groups. For example, in certain aspects, the accuracy of such methods may approach about 70%, about 75%, about 80%, about 82.5%, about 85%, about 87.5%, about 88%, about 90%, about 92.5%, about 95%, about 97.5%, about 98%, about 99% or more by evaluating the differential expression of more genes from the set (e.g., the set of genes set forth in Tables 5, 6, 12, 13 or 21).
In some embodiments, the diagnosis of lung cancer is made by comparing the expression of the genes or groups of genes set forth in, for example Table 12 or Table 21, by the subject's nasal epithelial cells to a control subject or a control group (e.g., a positive control with a confirmed diagnosis of lung cancer). Similarly, in certain aspects, the determination of a subject's smoking status is made by comparing the expression of the genes or groups of genes from the subject's nasal epithelial cells to a control subject or a control group (e.g., a non-smoker negative control). In certain embodiments, an appropriate control is an expression level (or range of expression levels) of a particular gene that is indicative of a known lung cancer status. An appropriate reference can be determined experimentally by a practitioner of the methods disclosed herein or may be a pre-existing expression value or range of values. When an appropriate control is indicative of lung cancer, a lack of a detectable difference (e.g., lack of a statistically significant difference) between an expression level determined from a subject in need of characterization or diagnosis of lung cancer and the appropriate control may be indicative of lung cancer in the subject. When an appropriate control is indicative of lung cancer, a difference between an expression level determined from a subject in need of characterization or diagnosis of lung cancer and the appropriate reference may be indicative of the subject being free of lung cancer.
Alternatively, an appropriate control may be an expression level (or range of expression levels) of one or more genes that is indicative of a subject being free of lung cancer. For example, an appropriate control may be representative of the expression level of a particular set of genes in a reference (control) biological sample obtained from a subject who is known to be free of lung cancer. When an appropriate control is indicative of a subject being free of lung cancer, a difference between an expression level determined from a subject in need of diagnosis of lung cancer and the appropriate reference may be indicative of lung cancer in the subject. Alternatively, when an appropriate reference is indicative of the subject being free of lung cancer, a lack of a detectable difference (e.g., lack of a statistically significant difference) between an expression level determined from a subject in need of diagnosis of lung cancer and the appropriate reference level may be indicative of the subject being free of lung cancer.
The control groups can be or comprise one or more subjects with a positive lung cancer diagnosis, a negative lung cancer diagnosis, non-smokers, smokers and/or former smokers. Preferably, the genes or their expression products in the nasal epithelial cell sample of the subject are compared relative to a similar group, except that the members of the control groups may not have lung cancer. For example, such a comparison may be performed in the nasal epithelial cell sample from a smoker relative to a control group of smokers who do not have lung cancer. Such a comparison may also be performed, e.g., in the nasal epithelial cell sample from a non-smoker relative to a control group of non-smokers who do not have lung cancer. Similarly, such a comparison may be performed in the nasal epithelial cell sample from a former smoker or a suspected smoker relative to a control group of smokers who do not have lung cancer. The transcripts or expression products are then compared against the control to determine whether increased expression or decreased expression can be observed, which depends upon the particular gene or groups of genes being analyzed, as set forth, for example, in Table 12 or Table 21. In certain embodiments, at least 50% of the gene or groups of genes subjected to expression analysis must provide the described pattern. Greater reliability is obtained as the percent approaches 100%. Thus, in one embodiment, at least about 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99% of the one or more genes subjected to expression analysis demonstrate an altered expression pattern that is indicative of the presence or absence of lung cancer, as set forth in, for example, Table 12 or Table 21. Similarly, in one embodiment, at least about 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99% of the one or more genes subjected to expression analysis demonstrate an altered expression pattern that is indicative of the subject's smoking status, as set forth in, for example, Table 5 or Table 6.
Any combination of the genes and/or transcripts of Table 12 or Table 21 can be used in connection with the assays and methods disclosed herein. In one embodiment, any combination of at least 5-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100, 100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190, 190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260, 260-270, 270-280, 280-290, 290-300, 300-310, 310-320, 320-330, 330-340, 340-350, 350-360, 360-370, 370-380, 380-390, 390-400, 400-410, 410-420, 420-430, 430-440, 440-450, 450-460, 460-470, 470-480, 480-490, 490-500, 500-510, 510-520, 520-530, and up to about 535 genes selected from the group consisting of genes or transcripts as shown in the Table 12 or Table 21.
The analysis of the gene expression of one or more genes may be performed using any gene expression methods known to one skilled in the art. Such methods include, but are not limited to expression analysis using nucleic acid chips (e.g. Affymetrix chips) and quantitative RT-PCR based methods using, for example real-time detection of the transcripts. Analysis of transcript levels according to the present invention can be made using total or messenger RNA or proteins encoded by the genes identified in the diagnostic gene groups of the present invention as a starting material. In certain embodiments the analysis is or comprises an immunohistochemical analysis with an antibody directed against proteins comprising at least about 10-20, 20-30, preferably at least 36, at least 36-50, 50, about 50-60, 60-70, 70-80, 80-90, 96, 100-180, 180-200, 200-250, 250-300, 300-350, 350-400, 400-450, 450-500, 500-535 proteins encoded by the genes and/or transcripts as shown in Table 12 or Table 21.
In one embodiment, the analysis is performed analyzing the amount of proteins encoded by one or more of the genes listed in Table 12 or Table 21 and present in the sample. In one embodiment the analysis is performed using DNA by analyzing the gene expression regulatory regions of the airway transcriptome genes using nucleic acid polymorphisms, such as single nucleic acid polymorphisms or SNPs, wherein polymorphisms known to be associated with increased or decreased expression are used to indicate increased or decreased gene expression in the individual. In one embodiment, the present invention uses a minimally invasive sample procurement method for obtaining nasal epithelial cell RNA (e.g., mRNA) that can be analyzed by expression profiling, for example, by array-based gene expression profiling. These methods can be used to determine if nasal epithelial cell gene expression profiles are affected by cancer. The methods disclosed herein can also be used to identify patterns of gene expression that are diagnostic of lung disorders/diseases, for example, cancer, and to identify subjects at risk for developing lung cancer. All or a subset of the genes identified according to the methods described herein can be used to design an array, for example, a microarray, specifically intended for the diagnosis or prediction of lung disorders or susceptibility to lung disorders. The efficacy of such custom-designed arrays can be further tested, for example, in a large clinical trial of smokers.
In some embodiments, the gene expression levels are determined by RT-PCR, DNA microarray hybridization, RNASeq, or a combination thereof. In some embodiments, one or more of the gene expression products is labeled. For example, a mRNA (or a cDNA made from such an mRNA) from a nasal epithelial cell sample may be labeled.
The methods of analyzing expression and/or determining an expression profile of the one or more genes include, for example, Northern-blot hybridization, ribonuclease protection assay, and reverse transcriptase polymerase chain reaction (RT-PCR) based methods. In certain aspects, the different RT-PCR based techniques are a suitable quantification method for diagnostic purposes of the present invention, because they are very sensitive and thus require only a small sample size which is desirable for a diagnostic test. A number of quantitative RT-PCR based methods have been described and are useful in measuring the amount of transcripts according to the present invention. These methods include RNA quantification using PCR and complementary DNA (cDNA) arrays (Shalon, et al., Genome Research 6(7):639-45, 1996; Bernard, et al., Nucleic Acids Research 24(8): 1435-42, 1996), real competitive PCR using a MALDI-TOF Mass spectrometry based approach (Ding, et al., PNAS, 100: 3059-64, 2003), solid-phase mini-sequencing technique, which is based upon a primer extension reaction (U.S. Pat. No. 6,013,431, Suomalainen, et al., Mol. Biotechnol. Jun; 15(2): 123-31, 2000), ion-pair high-performance liquid chromatography (Doris, et al., J. Chromatogr. A May 8; 806(1):47-60, 1998), and 5′ nuclease assay or real-time RT-PCR (Holland, et al., Proc Natl Acad Sci USA 88: 7276-7280, 1991).
Additional approaches to assess gene expression of the one or more genes are known in the art and may include but are not limited to one or more of the following: additional cytological assays, assays for specific proteins or enzyme activities, assays for specific expression products including protein or RNA or specific RNA splice variants, in situ hybridization, whole or partial genome expression analysis, microarray hybridization assays, serial analysis of gene expression (SAGE), enzyme linked immunoabsorbance assays, mass-spectrometry, immunohistochemistry, blotting, sequencing, RNA sequencing, DNA sequencing (e.g., sequencing of cDNA obtained from RNA); Next-Gen sequencing, nanopore sequencing, pyrosequencing, or Nanostring sequencing. For example, gene expression product levels may be determined according to the methods described in Kim, et. al. (Lancet Respir Med. 2015 June; 3(6):473-82, incorporated herein in its entirety, including all supplements). As used herein, the terms “assaying” or “detecting” or “determining” are used interchangeably in reference to determining gene expression product levels, and in each case, it is contemplated that the above-mentioned methods of determining gene expression product levels are suitable for detecting or assaying gene expression product levels. Gene expression product levels may be normalized to an internal standard such as total mRNA or the expression level of a particular gene including but not limited to glyceraldehyde 3 phosphate dehydrogenase, or tubulin.
In various embodiments, a sample comprises cells harvested from a tissue, e.g., in some embodiments the sample comprises cells harvested from a nasal epithelial cell sample. In certain embodiments, the cells may be harvested from a sample using standard techniques known in the art or disclosed herein. For example, in one embodiment, cells are harvested by centrifuging a cell sample and re-suspending the pelleted cells. The cells may be re-suspended in a buffered solution such as phosphate-buffered saline (PBS). After centrifuging the cell suspension to obtain a cell pellet, the cells may be lysed to extract nucleic acid, e.g., messenger RNA. All samples obtained from a subject, including those subjected to any sort of further processing, are considered to be obtained from the subject.
The sample, in one embodiment, is further processed before detection of the gene expression products is performed as described herein. For example, mRNA in a cell or tissue sample may be separated from other components of the sample. The sample may be concentrated and/or purified to isolate mRNA in its non-natural state, as the mRNA is not in its natural environment. For example, studies have indicated that the higher order structure of mRNA in vivo differs from the in vitro structure of the same sequence (see, e.g., Rouskin et al. (2014). Nature 505, pp. 701-705, incorporated herein in its entirety for all purposes).
mRNA from the sample in one embodiment, is hybridized to a synthetic DNA probe, which in some embodiments, includes a detection moiety (e.g., detectable label, capture sequence, barcode reporting sequence). Accordingly, in these embodiments, a non-natural mRNA-cDNA complex is ultimately made and used for detection of the gene expression product. In another embodiment, mRNA from the sample is directly labeled with a detectable label, e.g., a fluorophore. In a further embodiment, the non-natural labeled-mRNA molecule is hybridized to a cDNA probe and the complex is detected.
In one embodiment, once the mRNA is obtained from a sample, it is converted to complementary DNA (cDNA) in a hybridization reaction or is used in a hybridization reaction together with one or more cDNA probes. cDNA does not exist in vivo and therefore is a non-natural molecule. Furthermore, cDNA-mRNA hybrids are synthetic and do not exist in vivo. Besides cDNA not existing in vivo, cDNA is necessarily different than mRNA, as it includes deoxyribonucleic acid and not ribonucleic acid. The cDNA is then amplified, for example, by the polymerase chain reaction (PCR) or other amplification method known to those of ordinary skill in the art. For example, other amplification methods that may be employed include the ligase chain reaction (LCR) (Wu and Wallace, Genomics, 4:560 (1989), Landegren et al., Science, 241:1077 (1988), incorporated by reference in their entirety for all purposes, transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA, 86:1173 (1989), incorporated by reference in its entirety for all purposes), self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA, 87:1874 (1990), incorporated by reference in its entirety for all purposes), incorporated by reference in its entirety for all purposes, and nucleic acid based sequence amplification (NASBA). Guidelines for selecting primers for PCR amplification are known to those of ordinary skill in the art. See, e.g., McPherson et al., PCR Basics: From Background to Bench, Springer-Verlag, 2000, incorporated by reference in their entirety for all purposes. The product of this amplification reaction, i.e., amplified cDNA is also necessarily a non-natural product. First, as mentioned above, cDNA is a non-natural molecule. Second, in the case of PCR, the amplification process serves to create hundreds of millions of cDNA copies for every individual cDNA molecule of starting material. The number of copies generated are far removed from the number of copies of mRNA that are present in vivo.
In one embodiment, cDNA is amplified with primers that introduce an additional DNA sequence (e.g., adapter, reporter, capture sequence or moiety, barcode) onto the fragments (e.g., with the use of adapter-specific primers), or mRNA or cDNA gene expression product sequences are hybridized directly to a cDNA probe comprising the additional sequence (e.g., adapter, reporter, capture sequence or moiety, barcode). Amplification and/or hybridization of mRNA to a cDNA probe therefore serves to create non-natural double stranded molecules from the non-natural single stranded cDNA, or the mRNA, by introducing additional sequences and forming non-natural hybrids. Further, as known to those of ordinary skill in the art, amplification procedures have error rates associated with them. Therefore, amplification introduces further modifications into the cDNA molecules. In one embodiment, during amplification with the adapter-specific primers, a detectable label, e.g., a fluorophore, is added to single strand cDNA molecules. Amplification therefore also serves to create DNA complexes that do not occur in nature, at least because (i) cDNA does not exist in vivo, (i) adapter sequences are added to the ends of cDNA molecules to make DNA sequences that do not exist in vivo, (ii) the error rate associated with amplification further creates DNA sequences that do not exist in vivo, (iii) the disparate structure of the cDNA molecules as compared to what exists in nature, and (iv) the chemical addition of a detectable label to the cDNA molecules.
In some embodiments, the expression of a gene expression product of interest is detected at the nucleic acid level via detection of non-natural cDNA molecules.
The gene expression products described herein include RNA comprising the entire or partial sequence of any of the nucleic acid sequences of interest, or their non-natural cDNA product, obtained synthetically in vitro in a reverse transcription reaction. The term “fragment” is intended to refer to a portion of the polynucleotide that generally comprise at least 10, 15, 20, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 800, 900, 1,000, 1,200, or 1,500 contiguous nucleotides, or up to the number of nucleotides present in a full length gene expression product polynucleotide disclosed herein. A fragment of a gene expression product polynucleotide will generally encode at least 15, 25, 30, 50, 100, 150, 200, or 250 contiguous amino acids, or up to the total number of amino acids present in a full-length gene expression product protein of the invention.
In certain embodiments, a gene expression profile may be obtained by whole transcriptome shotgun sequencing (“WTSS” or “RNAseq”; see, e.g., Ryan et. al. BioTechniques 45: 81-94), which makes the use of high-throughput sequencing technologies to sequence cDNA in order to about information about a sample's RNA content. In general terms, cDNA is made from RNA, the cDNA is amplified, and the amplification products are sequenced.
After amplification, in some embodiments, the cDNA may be sequenced using any convenient method. For example, the fragments may be sequenced using Illumina's reversible terminator method, Roche's pyrosequencing method (454), Life Technologies' sequencing by ligation (the SOLiD platform) or Life Technologies' Ion Torrent platform. Examples of such methods are described in the following references: Margulies et al (Nature 2005 437: 376-80); Ronaghi et al (Analytical Biochemistry 1996 242: 84-9); Shendure (Science 2005 309: 1728); Imelfort et. al. (Brief Bioinform. 2009 10:609-18); Fox et. al. (Methods Mol Biol. 2009; 553:79-108); Appleby et. al. (Methods Mol Biol. 2009; 513: 19-39) and Morozova (Genomics. 2008 92:255-64), which are a; incorporated by reference for the general descriptions of the methods and the particular steps of the methods, including all starting products, reagents, and final products for each of the steps. As would be apparent, forward and reverse sequencing primer sites that compatible with a selected next generation sequencing platform may be added to the ends of the fragments during the amplification step.
In other embodiments, the products may be sequenced using nanopore sequencing (e.g. as described in Soni et. al. Clin Chem 53: 1996-2001 2007, or as described by Oxford Nanopore Technologies). Nanopore sequencing is a single-molecule sequencing technology whereby a single molecule of DNA is sequenced directly as it passes through a nanopore. A nanopore is a small hole, of the order of 1 nanometer in diameter. Immersion of a nanopore in a conducting fluid and application of a potential (voltage) across it results in a slight electrical current due to conduction of ions through the nanopore. The amount of current which flows is sensitive to the size and shape of the nanopore. As a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule obstructs the nanopore to a different degree, changing the magnitude of the current through the nanopore in different degrees. Thus, this change in the current as the DNA molecule passes through the nanopore represents a reading of the DNA sequence. Nanopore sequencing technology as disclosed in U.S. Pat. Nos. 5,795,782, 6,015,714, 6,627,067, 7,238,485 and 7,258,838 and U.S. patent application publications US2006003171 and US20090029477.
In some embodiments, the gene expression product of the subject methods is a protein, and the amount of protein in a particular biological sample may be analyzed using a classifier derived from protein data obtained from cohorts of samples. The amount of protein may be determined by one or more of the following: enzyme-linked immunosorbent assay (ELISA), mass spectrometry, blotting, or immunohistochemistry.
In some embodiments, gene expression product markers and alternative splicing markers may be determined by microarray analysis using, for example, Affymetrix arrays, cDNA microarrays, oligonucleotide microarrays, spotted microarrays, or other microarray products from Biorad, Agilent, or Eppendorf. Microarrays provide particular advantages because they may contain a large number of genes or alternative splice variants that may be assayed in a single experiment. In some cases, the microarray device may contain the entire human genome or transcriptome or a substantial fraction thereof allowing a comprehensive evaluation of gene expression patterns, genomic sequence, or alternative splicing. Markers may be found using standard molecular biology and microarray analysis techniques as described in Sambrook Molecular Cloning a Laboratory Manual 2001 and Baldi, P., and Hatfield, W. G., DNA Microarrays and Gene Expression 2002.
Microarray analysis generally begins with extracting and purifying nucleic acid from a biological sample, (e.g. a biopsy or fine needle aspirate) using methods known to the art. For expression and alternative splicing analysis it may be advantageous to extract and/or purify RNA from DNA. It may further be advantageous to extract and/or purify niRNA from other forms of RNA such as tRNA and rRNA.
Purified nucleic acid may further be labeled with a fluorescent label, radionuclide, or chemical label such as biotin, digoxigenin, or digoxin for example by reverse transcription, polymerase chain reaction (PCR), ligation, chemical reaction or other techniques. The labeling may be direct or indirect which may further require a coupling stage. The coupling stage can occur before hybridization, for example, using aminoallyl-UTP and NHS amino-reactive dyes (like cyanine dyes) or after, for example, using biotin and labelled streptavidin. In one example, modified nucleotides (e.g. at a 1 aaUTP: 4 TTP ratio) are added enzymatically at a lower rate compared to normal nucleotides, typically resulting in 1 every 60 bases (measured with a spectrophotometer). The aaDNA may then be purified with, for example, a column or a diafiltration device. The aminoallyl group is an amine group on a long linker attached to the nucleobase, which reacts with a reactive label (e.g. a fluorescent dye).
The labeled samples may then be mixed with a hybridization solution which may contain sodium dodecyl sulfate (SDS), SSC, dextran sulfate, a blocking agent (such as COT1 DNA, salmon sperm DNA, calf thymus DNA, PolyA or PolyT), Denhardt's solution, formamine, or a combination thereof.
A hybridization probe is a fragment of DNA or RNA of variable length, which is used to detect in DNA or RNA samples the presence of nucleotide sequences (the DNA target) that are complementary to the sequence in the probe. The probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target. The labeled probe is first denatured (by heating or under alkaline conditions) into single DNA strands and then hybridized to the target DNA.
To detect hybridization of the probe to its target sequence, the probe is tagged (or labeled) with a molecular marker; commonly used markers are 32P or Digoxigenin, which is nonradioactive antibody-based marker. DNA sequences or RNA transcripts that have moderate to high sequence complementarity (e.g. at least 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or more complementarity) to the probe are then detected by visualizing the hybridized probe via autoradiography or other imaging techniques. Detection of sequences with moderate or high complementarity depends on how stringent the hybridization conditions were applied; high stringency, such as high hybridization temperature and low salt in hybridization buffers, permits only hybridization between nucleic acid sequences that are highly similar, whereas low stringency, such as lower temperature and high salt, allows hybridization when the sequences are less similar. Hybridization probes used in DNA microarrays refer to DNA covalently attached to an inert surface, such as coated glass slides or gene chips, and to which a mobile cDNA target is hybridized.
A mix comprising target nucleic acid to be hybridized to probes on an array may be denatured by heat or chemical means and added to a port in a microarray. The holes may then be sealed and the microarray hybridized, for example, in a hybridization oven, where the microarray is mixed by rotation, or in a mixer. After an overnight hybridization, non-specific binding may be washed off (e.g. with SDS and SSC). The microarray may then be dried and scanned in a machine comprising a laser that excites the dye and a detector that measures emission by the dye. The image may be overlaid with a template grid and the intensities of the features (e.g. a feature comprising several pixels) may be quantified.
Various kits may be used for the amplification of nucleic acid and probe generation of the subject methods. Examples of kit that may be used in the present invention include but are not limited to Nugen WT-Ovation FFPE kit, cDNA amplification kit with Nugen Exon Module and Frag/Label module. The NuGEN WT-Ovation™ FFPE System V2 is a whole transcriptome amplification system that enables conducting global gene expression analysis on the vast archives of small and degraded RNA derived from FFPE samples. The system is comprised of reagents and a protocol required for amplification of as little as 50 ng of total FFPE RNA. The protocol may be used for qPCR, sample archiving, fragmentation, and labeling. The amplified cDNA may be fragmented and labeled in less than two hours for GeneChip™ 3′ expression array analysis using NuGEN's FL-Ovation™ cDNA Biotin Module V2. For analysis using Affymetrix GeneChip™ Exon and Gene ST arrays, the amplified cDNA may be used with the WT-Ovation Exon Module, then fragmented and labeled using the FL-Ovation™ cDNA Biotin Module V2. For analysis on Agilent arrays, the amplified cDNA may be fragmented and labeled using NuGEN's FL-Ovation™ cDNA Fluorescent Module.
In some embodiments, Ambion WT-expression kit may be used. Ambion WT-expression kit allows amplification of total RNA directly without a separate ribosomal RNA (rRNA) depletion step. With the Ambion™ WT Expression Kit, samples as small as 50 ng of total RNA may be analyzed on Affymetrix™ GeneChip™ Human, Mouse, and Rat Exon and Gene 1.0 ST Arrays. In addition to the lower input RNA requirement and high concordance between the Affymetrix™ method and TaqMan™ real-time PCR data, the Ambion™ WT Expression Kit provides a significant increase in sensitivity. For example, a greater number of probe sets detected above background may be obtained at the exon level with the Ambion™ WT Expression Kit as a result of an increased signal-to-noise ratio. Ambion™ expression kit may be used in combination with additional Affymetrix labeling kit. In some embodiments, AmpTec Trinucleotide Nano mRNA Amplification kit (6299-A15) may be used in the subject methods. The ExpressArt™ TRinucleotide mRNA amplification Nano kit is suitable for a wide range, from 1 ng to 700 ng of input total RNA. According to the amount of input total RNA and the required yields of aRNA, it may be used for 1-round (input >300 ng total RNA) or 2-rounds (minimal input amount 1 ng total RNA), with aRNA yields in the range of >10 μg. AmpTec's proprietary TRinucleotide priming technology results in preferential amplification of mRNAs (independent of the universal eukaryotic 3′-poly(A)-sequence), combined with selection against rRNAs. More information on AmpTec Trinucleotide Nao mRNA Amplification kit may be obtained at amp-tec.com/products.htm. This kit may be used in combination with cDNA conversion kit and Affymetrix labeling kit.
The raw data may then be normalized, for example, by subtracting the background intensity and then dividing the intensities making either the total intensity of the features on each channel equal or the intensities of a reference gene and then the t-value for all the intensities may be calculated. More sophisticated methods, include z-ratio, loess and lowess regression and RMA (robust multichip analysis), such as for Affymetrix chips.
In some embodiments, the above described methods may be used for determining transcript expression levels for training (e.g., using a classifier training module) a classifier to differentiate whether a subject is a smoker or non-smoker. In some embodiments, the above described methods may be used for determining transcript expression levels for training (e.g., using a classifier training module) a classifier to differentiate whether a subject has cancer or no cancer, e.g., based upon such expression levels in a sample comprising cells harvested from a nasal epithelial cell sample.
The presently described gene expression profile can also be used to screen for subjects who are susceptible to or otherwise at risk for developing lung cancer. For example, a current smoker of advanced age (e.g., 70 years old) may be at an increased risk for developing lung cancer and may represent an ideal candidate for the assays and methods disclosed herein. Moreover, the early detection of lung cancer in such a subject may improve the subject's overall survival. Accordingly, in certain aspects, the assays and methods disclosed herein are performed or otherwise comprise an analysis of the subject's clinical risk factors for developing cancer. For example, one or more clinical risk factors selected from the group consisting of advanced age (e.g., age greater than about 40 years, 50 years, 55 years, 60 years, 65 years, 70 years, 75 years, 80 years, 85 years, 90 years or more), smoking status, the presence of a lung nodule greater than 3 cm on CT scan, the lesion or nodule location (e.g., centrally located, peripherally located or both) and the time since the subject quit smoking. In certain embodiments, the assays and methods disclosed herein further comprise a step of considering the presence of any such clinical risk factors to inform the determination of whether the subject has lung cancer or is at risk of developing lung cancer.
As used herein, a “subject” means a human or animal. Usually the animal is a vertebrate such as a primate, rodent, domestic animal or game animal. In certain embodiments, the subject is a mammal (e.g., a primate or a human). In particular embodiments, the subject is a human. The subject may be an infant, a toddler, a child, a young adult, an adult or a geriatric. The subject may be a smoker, a former smoker or a non-smoker. The subject may have a personal or family history of cancer. The subject may have a cancer-free personal or family history. The subject may exhibit one or more symptoms of lung cancer or other lung disorder (e.g., emphysema, COPD). For example, the subject may have a new or persistent cough, worsening of an existing chronic cough, blood in the sputum, persistent bronchitis or repeated respiratory infections, chest pain, unexplained weight loss and/or fatigue, or breathing difficulties such as shortness of breath or wheezing. The subject may have a lesion, which may be observable by computer-aided tomography or chest X-ray. The subject may be an individual who has undergone a bronchoscopy or who has been identified as a candidate for bronchoscopy (e.g., because of the presence of a detectable lesion or suspicious imaging result). The subject may be an individual who has undergone an indeterminate or non-diagnostic bronchoscopy. The subject may be an individual who has undergone an indeterminate or non-diagnostic bronchoscopy and who has been recommended to proceed with an invasive lung procedure (e.g., transthoracic needle aspiration, mediastinoscopy, lobectomy, or thoracotomy) based upon the indeterminate or non-diagnostic bronchoscopy. The terms, “patient” and “subject” are used interchangeably herein. In some embodiments, the subject is at risk for developing lung cancer. In some embodiments, the subject has lung cancer and the assays and methods disclosed herein may be used to monitor the progression of the subject's disease or to monitor the efficacy of one or more treatment regimens.
In certain aspects, the methods and assays disclosed herein are useful for determining a treatment course for a subject. For example, such methods and assays may involve determining the expression levels of one or more genes (e.g., one or more of the genes set forth in Table 12 or Table 21, or one or more or all of the genes set forth in Table 13) in a biological sample obtained from the subject, and determining a treatment course for the subject based on the expression profile of such one or more genes. In some embodiments, the treatment course is determined based on a lung cancer risk-score derived from the expression levels of the one or more genes analyzed. The subject may be identified as a candidate for a lung cancer therapy based on an expression profile that indicates the subject has a relatively high likelihood of having lung cancer. The subject may be identified as a candidate for an invasive lung procedure (e.g., transthoracic needle aspiration, mediastinoscopy, lobectomy, or thoracotomy) based on an expression profile that indicates the subject has a relatively high likelihood of having lung cancer (e.g., greater than 60%, greater than 70%, greater than 80%, greater than 90%). In certain aspects, a relatively high likelihood of having lung cancer means greater than about a 65% chance of having lung cancer. In certain aspects, a relatively high likelihood of having lung cancer means greater than about a 70% chance of having lung cancer. In certain aspects, a relatively high likelihood of having lung cancer means greater than about a 75% chance of having lung cancer. In certain aspects, a relatively high likelihood of having lung cancer means greater than about an 80-85% chance of having lung cancer. The subject may be identified as not being a candidate for a lung cancer therapy or an invasive lung procedure based on an expression profile that indicates the subject has a relatively low likelihood (e.g., less than 50%, less than 40%, less than 30%, less than 20%) of having lung cancer. In certain aspects, a relatively low likelihood of having lung cancer means less than about a 35% chance of having lung cancer. In certain aspects, a relatively low likelihood of having lung cancer means less than about a 30% chance of having lung cancer. In certain aspects, a relatively low likelihood of having lung cancer means less than about a 25% chance of having lung cancer. In certain aspects, a relatively low likelihood of having lung cancer means less than about a 35% chance of having lung cancer. In certain aspects, a relatively low likelihood of having lung cancer means less than about a 20-25% chance of having lung cancer. Accordingly, in certain aspects of the present inventions, if the methods disclosed herein are indicative of the subject having lung cancer or of being at risk of developing lung cancer, such methods may comprise a further step of treating the subject (e.g., administering to the subject a treatment comprising one or more of chemotherapy, radiation therapy, immunotherapy, surgical intervention and combinations thereof).
In certain aspects, if the methods and assays disclosed herein are indicative of a subject being at a higher risk of having or developing lung cancer, the subject may be subjected to more invasive monitoring, such as a direct tissue sampling or biopsy of the nodule, under the presumption that the positive test indicates a higher likelihood of the nodule is a cancer. Alternatively, on the basis of the methods and assays disclosed herein being indicative of a subject's higher risk of having or developing lung cancer, in certain embodiments an appropriate therapeutic regimen (e.g., chemotherapy or radiation therapy) may be administered to the subject. Conversely, in those instances where the methods and assays disclosed herein are indicative of a subject having a reduced risk of developing lung cancer, then in certain aspects the subject may be subjected to further confirmatory testing, such as further imaging surveillance (e.g., a repeat CT scan to monitor whether the nodule grows or changes in appearance before doing a more invasive procedure), or a determination made to withhold a particular treatment (e.g., chemotherapy or radiation therapy) on the basis of the subject's favorable or reduced risk of having or developing lung cancer. In some embodiments, the assays and methods disclosed herein may be used to confirm the results or findings from a more invasive procedure, such as direct tissue sampling or biopsy. For example, in certain aspects the assays and methods disclosed herein may be used to confirm or monitor the benign status of a previously biopsied nodule or lesion.
In some embodiments, the methods and assays disclosed herein are useful for determining a treatment course for a subject that has undergone an indeterminate or non-diagnostic bronchoscopy does not have lung cancer, wherein the method comprises determining the expression levels of one or more genes (e.g., one or more of the genes set forth in Table 12 or Table 21, or one or more or all of the genes set forth in Table 13) in a sample of nasal epithelial cells obtained from the subject, and determining whether the subject that has undergone an indeterminate or non-diagnostic bronchoscopy does or does not have lung cancer or is not at risk of developing lung cancer. In some such embodiments, the method comprises determining a lung cancer risk-score derived from the expression levels of the one or more genes analyzed. In particular embodiments, the subject that has undergone an indeterminate or non-diagnostic bronchoscopy would have typically been identified as being a candidate for an invasive lung procedure (e.g., transthoracic needle aspiration, mediastinoscopy, lobectomy, or thoracotomy) based upon such indeterminate of non-diagnostic bronchoscopy result, but the subject is instead identified as being a candidate for a non-invasive procedure (e.g., monitoring by CT scan) because the subjects expression levels of the one or more genes (e.g., one or more of the genes set forth in Table 12 or Table 21, or one or more or all of the genes set forth in Table 13) in the sample of nasal epithelial cells obtained from the subject indicates that the subject has a low risk of having lung cancer (e.g., in some embodiments the instant method indicates that the subject has a greater than 60% chance of not having cancer, or a greater than 70%, 80%, or greater than 90% chance of not having cancer). In some embodiments, the subject may be identified as a candidate for an invasive lung cancer therapy based on an expression profile that indicates the subject has a relatively high likelihood of having lung cancer (e.g., in some embodiments the instant method indicates that the subject has a greater than 60% chance of having cancer, or a greater than 70%, 80%, or greater than 90% chance of having cancer). Accordingly, in certain aspects of the present inventions, if the methods disclosed herein are indicative of the subject having lung cancer or of being at risk of developing lung cancer, such methods may comprise a further step of treating the subject (e.g., administering to the subject a treatment comprising one or more of chemotherapy, radiation therapy, immunotherapy, surgical intervention and combinations thereof).
In some cases, an expression profile is obtained and the subject is not indicated as being in the high risk or the low risk categories. In some embodiments, a health care provider may elect to monitor the subject and repeat the assays or methods at one or more later points in time, or undertake further diagnostics procedures to rule out lung cancer, or make a determination that cancer is present, soon after the subject's lung cancer risk determination was made. Also contemplated herein is the inclusion of one or more of the genes and/or transcripts presented in, for example, Table 5, Table 6, Table 12, Table 13, Table 14, Table 15 or Table 21, into a composition or a system for detecting lung cancer in a subject. For example, any one or more genes and or gene transcripts from Table 12, Table 13 or Table 21 may be added as a lung cancer marker for a gene expression analysis. In some aspects, the present inventions relate to compositions that may be used to determine the expression profile of one or more genes from a subject's biological sample comprising nasal epithelial cells. For example, compositions are provided that consist essentially of nucleic acid probes that specifically hybridize with one or more genes set forth in Table 12, Table 13 or Table 21. These compositions may also include probes that specifically hybridize with one or more control genes and may further comprise appropriate buffers, salts or detection reagents. In certain embodiments, such probes may be fixed directly or indirectly to a solid support (e.g., a glass, plastic or silicon chip) or a bead (e.g., a magnetic bead).
The compositions described herein may be assembled into diagnostic or research kits to facilitate their use in one or more diagnostic or research applications. In some embodiments, such kits and diagnostic compositions are provided that comprise one or more probes capable of specifically hybridizing to up to 5, up to 10, up to 25, up to 50, up to 100, up to 200, up to 300, up to 400, up to 500 or up to 535 genes set forth in Table 12, Table 13 or Table 21 or their expression products (e.g., mRNA). In some embodiments, each of the nucleic acid probes specifically hybridizes with one or more genes selected from those genes set forth in Table 12, Table 13 or Table 21, or with a nucleic acid having a sequence complementary to such genes. In some aspects, each of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 20 of the probes specifically hybridizes with one or more genes selected from group of set forth in Table 12, Table 13 or Table 21, or with a nucleic acid having a sequence complementary to such genes.
A kit may include one or more containers housing one or more of the components provided in this disclosure and instructions for use. Specifically, such kits may include one or more compositions described herein, along with instructions describing the intended application and the proper use and/or disposition of these compositions. Kits may contain the components in appropriate concentrations or quantities for running various experiments.
The articles “a” and “an” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to include the plural referents. Claims or descriptions that include “or” between one or more members of a group are considered satisfied if one, more than one, or all of the group members are present in, employed in, or otherwise relevant to a given product or process unless indicated to the contrary or otherwise evident from the context. The invention includes embodiments in which exactly one member of the group is present in, employed in, or otherwise relevant to a given product or process. The invention also includes embodiments in which more than one, or the entire group members are present in, employed in or otherwise relevant to a given product or process. Furthermore, it is to be understood that the invention encompasses all variations, combinations, and permutations in which one or more limitations, elements, clauses, descriptive terms, etc., from one or more of the listed claims is introduced into another claim dependent on the same base claim (or, as relevant, any other claim) unless otherwise indicated or unless it would be evident to one of ordinary skill in the art that a contradiction or inconsistency would arise. Where elements are presented as lists, (e.g., in Markush group or similar format) it is to be understood that each subgroup of the elements is also disclosed, and any element(s) can be removed from the group. It should be understood that, in general, where the invention, or aspects of the invention, is/are referred to as comprising particular elements, features, etc., certain embodiments of the invention or aspects of the invention consist, or consist essentially of, such elements, features, etc. For purposes of simplicity those embodiments have not in every case been specifically set forth in so many words herein. It should also be understood that any embodiment or aspect of the invention can be explicitly excluded from the claims, regardless of whether the specific exclusion is recited in the specification. The publications and other reference materials referenced herein to describe the background of the invention and to provide additional detail regarding its practice are hereby incorporated by reference.
Previous work from our lab has shown that bronchial and nasal epithelium exhibit a common physiological response to tobacco smoke exposure (Zhang, et al., Phys. Gen. 2011). Given this relationship and the demonstrated utility of bronchial gene expression as a diagnostic marker of lung cancer, the present inventors sought to test the hypothesis that the cancer-associated expression profiles observed in the bronchial airways might also be detectable in nasal epithelium. Detecting the cancer-associated airway field of injury via nasal epithelium would offer a faster, non-invasive and cheaper alternative to sampling bronchial epithelium, and thereby expand the clinical settings where airway gene expression would have utility in evaluating patients for lung cancer.
In the following studies, the present inventors identified genes with cancer-associated expression profiles in nasal epithelium using samples obtained from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer as part of the Airway Epithelium Gene Expression in the Diagnosis of Lung Cancer (AEGIS) clinical trials. The inventors demonstrated that the cancer-associated field of injury observed in bronchial epithelium extended to the nose and that nasal epithelial gene expression adds information about lung cancer that is distinct from clinical risk factors. These findings suggest that nasal gene expression may be useful in determining the cancer status of indeterminate pulmonary nodules.
To identify genes whose expression is associated with lung cancer status in nasal epithelium and to compare the relationship between nasal and bronchial cancer-associated gene expression, the present inventors used existing microarray data from 299 bronchial epithelium samples from patients in the AEGIS clinical trials (Whitney, et al., BMC Med Gen 2015) and generated novel microarray data from 554 nasal epithelium samples obtained from patients in the same trials. All samples were collected from consenting patients who were undergoing bronchoscopy for clinical suspicion on lung cancer. 424 nasal samples were collected from patients enrolled in the AEGIS-1 trial and 130 were from patients in the AEGIS-2 trial (
Differential expression analysis via linear modeling revealed 535 genes that were significantly associated with cancer status in our training set (p<0.001), as illustrated in
Several distinct patterns of gene co-expression were also observed within these 535 genes and consensus clustering identified four distinct co-expression clusters (Table 12). The smallest of the four clusters contained 43 genes that were up-regulated in samples from patients with cancer relative to controls. The other three clusters were down-regulated in patients with cancer relative to controls (
To summarize the behavior of each cluster, the average expression of all genes in a cluster was computed for each sample. Each of the four cancer cluster means was strongly associated with cancer status (p<0.001), as shown in Table 2, below. The present inventors assessed the gene functions enriched in each of these four clusters using the Reactome and GO databases accessed through the web-based program EnrichR (Chen, et al., 2013 BMC Bioinfo). A complete list of statistically significantly enriched pathways and GO categories (FDR<0.05) is shown in Tables 13, 22 and 23, below. Clusters 1, 2, and 3 were enriched for genes involved in the regulation of apoptosis, immune system signaling, and xenobiotic detoxification, respectively. Cluster 4 was enriched for genes involved in ion transport.
Given the strong concordance in smoking-related gene expression between nasal and bronchial epithelium, the present inventors next sought to determine if a shared pattern of cancer-related gene expression might exist between the nose and bronchus by leveraging microarray data from 299 bronchial epithelium samples obtained from AEGIS-1 patients (Whitney, et al., BMC Medical Genomics). One hundred and fifty-seven of the 299 bronchial samples came from the same patients as those in our nasal training set (Table 9 and
To further corroborate the hypothesis of a shared field of lung-cancer associated injury, the present inventors also examined the nasal expression patterns of genes previously found to be associated with lung cancer in bronchial epithelium (Whitney, et al., BMC Med Genomics 2015). Whitney, et al. previously reported a gene-expression signature of 232 genes grouped in 11 distinct co-expression clusters from bronchial epithelial samples that were strongly associated with the presence of lung cancer. Using the mean expression values of the genes in each of these clusters as a summary of the expression of each cluster in each patient, the present inventors found that eight of these clusters were significantly associated with the presence or absence of lung cancer (p<0.05) in the training set (Table 3, below). Among the clusters most associated with cancer were genes involved in cell cycle, response to retinoic acid, and the innate immune response (Table 3). Based on the concordant expression of cancer-associated genes in bronchial and nasal epithelium, the present inventors computed the bronchial lung cancer classifier risk score (Whitney, et al., BMC Med Gen 2015) for each of the samples in our nasal training set. The risk scores computed on matched bronchial and nasal samples were highly correlated (R=0.70, p<0.001, n=157) and the classifier had a sensitivity of 81% and AUC of 0.65 (p=8.1e-13, n=375) in the entire training set (
To determine if nasal gene expression could serve as a predictor of lung cancer status, the present inventors selected the thirty most statistically significantly differentially expressed genes (P<0.001) from among the 535 genes with cancer-associated nasal gene expression for use in a weighted-voting biomarker (Table 13). The biomarker panel size of 30 genes was chosen as the smallest number of genes that achieved maximal performance in cross-validation. This biomarker had an AUC of 0.69 (n=375, 95% CI=0.63 to 0.75, P<0.001) in cross validation in the training set. Twenty-two of the 30 genes were also statistically significantly correlated between matched bronchial and nasal samples (mean R=0.29, range=0.16-0.49, P<0.05). In order to evaluate the potential for the nasal gene expression biomarker to add to clinical risk factors for lung cancer detection, the present inventors developed a clinical risk factor model and tested whether incorporating the gene-expression biomarker enhanced its performance. The computation of the clinical factor model biomarker score was derived from the following model,
x=(−4.65244938)+(−0.24676442*SMK)+(−1.16932025*TSQ1)+(0.12091159*TSQ2)+(0.07136355*AGE)+(1.22446427*BMS1)+(2.65403176*BMS2),
where, SMK=1 if former smoker and 0 if current smoker, TSQ1=1 if time since quit smoking is >=15 years, and 0 otherwise, TSQ2=1 if time since quit smoking is unknown, and 0 otherwise, AGE=the patient's numeric age in years, BMS1=1 if patient's mass size is <3 cm, and 0 otherwise, and BMS2=1 if patient's mass size is >=3 cm, and 0 otherwise; then
where a patient is predicted cancer positive if the clinical factor model biomarker score is greater than 0.5823596, and cancer negative otherwise.
Gould previously identified smoking status, time since quit, age, and mass size as important clinical risk factors of lung cancer for patients with solitary pulmonary nodules (Gould, et al., Chest 2007). However, self-reported smoking status and time since quit which have been shown to be inconsistent with serum cotinine levels, especially in newly diagnosed lung cancer patients (Lewis, et al., Biomarkers 2003; Morales, et al., CCC 2013) and the inventors therefore used an approach similar to that described by Whitney, et al., to identify gene expression profiles that could serve as their surrogates. Two logistic regression models, including 5 and 2 genes, respectively, were derived in the training set to predict smoking status and time since quit (<15y, ≥15y) (Tables 14 and 15), where the equations associated with Tables 14 and 15 are respectively shown below,
These classifiers had AUC values of 0.89 (p<2.2e-16, n=375) and 0.75 (p=0.0001, n=319) in the training set, respectively. Consistent with what has been reported for bronchial epithelial gene expression, the present inventors could not identify a gene expression predictor of patient age (Whitney, et al., BMC Med Gen 2015); nor were the present inventors able to identify a robust gene expression correlate of mass size. Collectively, the gene expression correlates for smoking status and time since quit as well as numerical age and categorized mass size (<3 cm, ≥3 cm, infiltrates) were used to model lung cancer using logistic regression in the training set (Table 16) and derived from the following model, where
x=−5.14689+(Genomic_Smoking_Status_Score*1.82244)+(Genomic_Time_Since_Quit_Score*2.31235)+(AGE*0.04947)+(BMS1*1.27246)+(BMS2*2.59898),
where, AGE=the patient's numeric age in years, BMS1=1 if patient's mass size is <3 cm, and 0 otherwise, BMS2=1 if patient's mass size is >=3 cm, and 0 otherwise, and
where a patient is predicted cancer positive if clinical risk factors with genomic correlates model score is greater than 0.4969356, and cancer negative otherwise.
These risk factors were further combined with the cancer-associated gene expression classifier into a single logistic regression clinicogenomic classifier, the parameters of which were also derived in the training set (Table 17) and from the following model, where,
x=−4.1504024+(Genomic_Smoking_Status_Score*0.7534516)+(Genomic_Time_Since_Quit_Score*0.3276714)+(Genomic_Cancer_Classifier_Score*0.6629011)+(AGE*0.0452670)+(BMS1*1.3423457)+(BMS2*2.6932782),
where, AGE=the patient's numeric age in years, BMS1=1 if patient's mass size is <3 cm, and 0 otherwise, BMS2=1 if patient's mass size is >=3 cm, and 0 otherwise, and
where a patient is predicted cancer positive if clinicogenomic with genomic correlates model score is greater than 0.4590236, and cancer negative otherwise.
The performance of the clinical and clinicogenomic models was evaluated using an independent set of nasal samples (n=130) from the AEGIS-2 clinical trial that were not used in the development of either classifier. The clinicogenomic model yielded an AUC of 0.80 in the validation set which was significantly higher than the AUC of 0.76 achieved by the clinical risk factor model alone (p=0.05). Operating points for binary classification in both models were chosen to achieve 50% specificity in the training set. The addition of cancer-associated gene expression to the clinical risk-factor model resulted in a significant increase in sensitivity from 0.85 to 0.94 (p=0.04) and increase in negative predictive value from 0.73 to 0.87 (Table 18). Importantly, the clinicogenomic model showed improvements in sensitivity from 63% to 88% over the clinical model in subjects with lesion size <3 cm and showed stable or improved performance in patients with lesions >3 cm or ill-defined infiltrates (Table 18). Consistently higher sensitivity was also observed with the clinicogenomic model in patients with central and/or peripheral nodules compared to the clinical model (Table 19). Furthermore, the addition of cancer-associated gene expression to clinical risk factors improved prediction sensitivity across all stages and cell types of disease (Table 20). Collectively, these data suggest that nasal gene expression captures molecular information about the likelihood of lung cancer that is independent of clinical factors and therefore has the potential to improve lung cancer detection.
In an alternative approach, the present inventors built clinical and clinicogenomic models that used reported clinical values instead of a mixture of reported clinical values and gene-expression predicted clinical values as in Example 3. In choosing which clinical risk factors to include, the present inventors again relied on a study in which Gould et al. identified smoking status, time since quit, age, and mass size as important clinical risk factors of lung cancer for patients with solitary pulmonary nodules (Gould, et al., Chest 2007). Patient age, smoking status (current, former), time since quit (≤15 years, >15 years, unknown), and categorized mass size (<3 cm, ≥3 cm, infiltrates) were used to create a clinical risk factor model for lung cancer using logistic regression. The training set for this model consisted of the nasal training set used to derive the gene expression classifier as well as clinical data from an additional 142 patients from the AEGIS-1 cohort for a total training set of 517 patients for the clinical model (see,
Gene_1_score=−0.076842874545387*(Expression_of_probeset_8091385−10.223361024585)
Gene_2_score=−0.066812409800121*(Expression_of_probeset_8115147−10.4979919874352)
Gene_3_score=−0.0508738437722716*(Expression_of_probeset_8034420−7.74862668913246)
Gene_4_score=−0.0853002904314322*(Expression_of_probeset_8075720−6.02260696919916)
Gene_5_score=−0.0663441276969046*(Expression_of_probeset_7940775−8.60283524794079)
Gene_6_score=−0.100361459561592*(Expression_of_probeset_8125463−5.76219176807997)
Gene_7_score=−0.0731786032726885*(Expression_of_probeset_7912638−5.80836005908298)
Gene_8_score=−0.0588577574308188*(Expression_of_probeset_7978123−7.81869896068138)
Gene_9_score=−0.0291537526685959*(Expression_of_probeset_7937217−7.99754044283416)
Gene_10_score=−0.059579001469581*(Expression_of_probeset_8002133−6.76231617487145)
Gene_11_score=−0.0539204890593068*(Expression_of_probeset_8084895−9.25452952745888)
Gene_12_score=−0.0435216311590311*(Expression_of_probeset_8180166−9.66750825451152)
Gene_13_score=−0.102616463622019*(Expression_of_probeset_8179331−5.87582547195644)
Gene_14_score=−0.256702735040285*(Expression_of_probeset_8146092−6.84033653454892)
Gene_15_score=−0.0471515312903042*(Expression_of_probeset_7898115−6.1806473478809)
Gene_16_score=−0.0978767707892084*(Expression_of_probeset_8117476−6.42634821287224)
Gene_17_score=−0.112823826752702*(Expression_of_probeset_8180078−7.19373066084955)
Gene_18_score=−0.0489348626366957*(Expression_of_probeset_8092978−10.4325518383754)
Gene_19_score=−0.042561683753686*(Expression_of_probeset_7925876 −7.26663202627375)
Gene_20_score=−0.040517314218441*(Expression_of_probeset_7940160−8.41904220936401)
Gene_21_score=−0.0255314067182751*(Expression_of_probeset_8076998−9.90620981343659)
Gene_22_score=−0.0298478887838912*(Expression_of_probeset_8179041−11.3092804247355)
Gene_23_score=−0.152455958242676*(Expression_of_probeset_8145317−4.99539634280867)
Gene_24_score=−0.0733338563077433*(Expression_of_probeset_8180049−6.54533529834041)
Gene_25_score=−0.0563089183829938*(Expression_of_probeset_7993195−6.13360660846907)
Gene_26_score=−0.0595673359556534*(Expression_of_probeset_7929882−5.9425809217138)
Gene_27_score=−0.0292004329271551*(Expression_of_probeset_8179049−10.6201119280024)
Gene_28_score=−0.0421648259067651*(Expression_of_probeset_7947815−7.74324780382519)
Gene_29_score=−0.0815827122613575*(Expression_of_probeset_8096070−7.28569239691227)
Gene_30_score=−0.0326333009894926*(Expression_of_probeset_8063000−10.9610191238719),
where,
Genomic Cancer Classifier Score=Gene1
and the clinicogenomic biomarker score was derived using the following equation,
x=(−3.56652108)+(−0.01621785*SMK)+(−0.24792934*TSQ1)+(0.52981359*TSQ2)+(0.04180910*AGE)+(1.29057600*BMS1)+(2.70293937*BMS2)+(0.68513004*Genomic_cancer_classifier_score),
where SMK=1 if former smoker and 0 if current smoker, TSQ1=1 if time since quit smoking is >=15 years, and 0 otherwise, TSQ2=1 if time since quit smoking is unknown, and 0 otherwise, AGE=the patient's numeric age in years, BMS1=1 if patient's mass size is <3 cm, and 0 otherwise BMS2=1 if patient's mass size is >=3 cm, and 0 otherwise; then
where a patient is predicted cancer positive if the clinicogenomic model biomarker score is greater than 0.4673243, and cancer negative otherwise.
The performance of the clinical and clinicogenomic models was evaluated using an independent set of nasal samples (n=130) from the AEGIS-2 clinical trial that were not used in the development of the classifier. The clinicogenomic model yielded an AUC of 0.81 (95% CI=0.74 to 0.89) in the validation set, which was statistically significantly higher than the AUC of 0.74 (95% CI=0.66 to 0.83) achieved by the clinical risk-factor model alone (P=0.01) (
In the foregoing studies, the present inventors explored whether the airway field of injury in lung cancer extends to nasal epithelium and determined that there are gene expression alterations in the nasal epithelium of patients with lung cancer compared to those with benign diagnoses. It was observed that the lung cancer-associated gene expression patterns previously identified in the bronchial epithelium are highly concordant with those observed in nasal epithelium. Finally, the present inventors showed that the addition of nasal gene expression to clinical risk factors of disease improves diagnostic sensitivity and negative predictive value of a clinical factor model. These findings strengthen the “field of injury” hypothesis in which lung disease is able to influence the gene expression phenotype of normal-appearing cells throughout the airway; and perhaps more excitingly, suggest the potential for biomarkers based on nasal epithelial gene expression that could be used for lung cancer detection.
While previous studies have validated the existence of bronchial airway gene expression alterations in patients with lung cancer and demonstrated their clinical utility in lung cancer detection (Silvestri, et al. NEJM 2015), little is known about the physiological processes responsible for this “field of injury.” One hypothesis for the presence of lung cancer-associated alterations in nasal and bronchial gene expression is that the subset of smokers who develop lung cancer exhibit a distinct genomic response to tobacco smoke exposure throughout all airway epithelial cells, consistent with the “etiological field effect” described by Lochhead, et al. for colon and other cancer types (Lochhead, et al., Mod Pathol. 2015). This paradigm suggests that the airway gene-expression signature is a risk marker for lung cancer as opposed to a direct consequence of the presence of lung cancer based on local or systemic factors produced by the tumor or its microenvironment (i.e., the “conventional field effect” defined by Lochhead, et al., Mod Pathol. 2015). Consistent with the etiological field effect hypothesis, the present inventors observed a concordant downregulation of genes associated with immune system activation in patients with lung cancer in both bronchial and nasal epithelium, which might suggest that an impaired immune response sets the stage for tumorigenesis in the lung microenvironment. Alternatively, despite the distance to the tumor, these cancer-associated gene expression differences may be a direct result of factors secreted by the tumor or its microenvironment, or some other consequence of the presence of the tumor consistent with the “conventional field effect” described above.
Mechanistically, it is intriguing that a number of genes with important roles in cancer-related processes are among the differentially expressed genes. Of the genes that were downregulated in patients with lung cancer, CASP10 and CD177 were among the most correlated genes between bronchial and nasal epithelium and are associated with the induction of apoptosis and activation of the immune response, respectively. The present inventors also identified a number of genes involved in the p53 pathway that were downregulated in patients with lung cancer, including BAK1, ST14, CD82, and MUC4. BAK1 is associated with the induction of apoptosis (Rosell, et al., The Lancet 2013; Gu, et al., Tumor Biol. 2014) and has been previously shown to be downregulated in the tumors of patients with non-small cell lung cancer (NSCLC) (Singhal, et al., Lung Cancer. 2008; 60(3):313-324.). ST14 has been described as a tumor suppressor in breast cancer and its overexpression associated with the inhibition of tumor cell migration and cell invasion (Wang, et al., J Biol Chem. 2009). The downregulation of CD82, which is a metastasis suppressor in prostate cancer (Dong, et al. Science 1995), has been shown to be correlated with poor survival in patients with lung adenocarcinoma (Adachi, et al., Cancer Res. 1996). MUC4, whose downregulation has been associated with increased tumor stage and poorer overall survival, has also been shown to play an oncogenic role in multiple cancers and is a tumor suppressor in NSCLC, acting as a modifier of p53 expression (Majhi, et al., J Thorac Oncol Off Publ Int Assoc Study Lung Cancer. 2013).
From a clinical perspective, the present inventors found that the addition of lung cancer-associated gene expression to established clinical risk factors improved the sensitivity and negative predictive value for detecting lung cancer; these are the key performance metrics for driving potential clinical utility in this setting (e.g., allowing physicians to avoid unnecessary invasive procedures in those with benign disease). This provides the first proof of concept for the use of nasal gene expression for lung cancer detection. The present inventors elected to establish the presence of a nasal field of lung cancer-associated injury using samples from the AEGIS trial given the unique availability of matched bronchial samples, despite the fact that these patients were undergoing bronchoscopy for suspected lung cancer. The demonstration of the added value of nasal gene expression for lung cancer detection in this setting sets the stage for the development of nasal gene expression biomarkers for lung cancer in other clinical settings where bronchoscopy is not frequently used because of lesion or nodule size or location, risk of complications, or cost. In particular, it will now be of interest to develop nasal biomarkers for patients with small peripheral nodules found incidentally or via screening as our current bronchoscopy-based cohort is enriched for patients with centrally located lesions. In the clinical setting of patients with small peripheral nodules, it is envisioned that a nasal biomarker for lung cancer with a low negative likelihood ratio (on par with the NLR observed by the present inventors for the nasal biomarker in the AEGIS samples) could be used to identify nodule patients who are at low risk of malignancy and can be managed by CT surveillance.
Our demonstration of a nasal field of injury for lung cancer extends our previous work which demonstrated a smoking-induced field of injury that is highly concordant between bronchial and nasal epithelium (Zhang, et al., Phys. Gen. 2011). In this study, the present inventors present multiple lines of evidence that the lung cancer-associated field of injury detectable in bronchial airway epithelium (Whitney, et al., BMC Med Gen 2015) is similarly altered in nasal epithelium. The present inventors also demonstrated both that the genes whose expression is altered in patients with cancer are highly concordant in bronchial and nasal epithelium and that they are involved in similar biological processes including the innate immune response, response to retinoic acid, cell cycle, and xenobiotic detoxification. Furthermore, the present inventors also show that a lung cancer gene expression biomarker developed for use with bronchial gene expression data was able to distinguish patients with and without cancer when used with nasal instead of bronchial data.
Despite the similarity between bronchial and nasal cancer-associated gene expression, there were also differences identified. The present inventors found some lung-cancer associated genes and pathways that are either nasal- or bronchial-specific (e.g. the decreased expression of genes involved in apoptosis in nasal epithelium from patients with lung cancer). The present inventors also found that we were able to achieve better biomarker performance in independent nasal data when we developed and trained the biomarker using nasal data. The presence of some differences between bronchial and nasal epithelial cancer-associated gene expression was consistent with our previous findings with regard to smoking—where most genes are similarly altered in bronchial and nasal epithelium and a minority were airway-location specific (Zhang, et al., Phys. Gen. 2011). Given the concordance of gene expression in the context of both lung cancer and cigarette smoke exposure, one could envision expanding the airway field of injury concept for the monitoring and treatment of other diseases such as chronic obstructive pulmonary disease (COPD).
The importance and potential impact of the foregoing studies derive from several key strengths. First, the patients came from a large number of academic and community hospitals and reflect a variety of practice settings and different geographical locales; thus the diversity of alternative benign diagnoses is represented. Second, the training and validation sets came from two separate clinical trials, which minimized the potential for the model to depend on locally confounding variables. Third, the samples were prospectively collected and cancer status was unknown at the time of collection. Fourth, the present inventors have shown that nasal gene expression identifies a source of lung cancer risk that is independent of major clinical risk factors. Rather than serving as an alternative to bronchoscopy, the present inventors envision that a nasal biomarker for lung cancer could be used more broadly to distinguish the subset of patients who might benefit from bronchoscopy or other invasive procedures from those whose imaging abnormalities can be managed by repeat imaging.
While the sensitivity of our nasal clinicogenomic classifier was high (88%) in patients with nodules less than 3 cm in our validation set, the number of patients in that subgroup was small (n=54) and further studies are needed to both validate this performance as well as determine if similar levels of performance are attained in the broader clinical setting where this test would ultimately be used.
Second, while we found that nasal gene expression is an independent predictor of lung cancer compared to clinical factors alone, the performance of our nasal classifier was not dramatically different from a clinical factor biomarker. The present inventors hypothesized that this finding stems in large part from the cohort characteristics (the high pre-test probability of cancer making clinical factors such as nodule size very predictive of lung cancer) and that in a lower cancer prevalence setting, such as indeterminate pulmonary nodules, the relative contribution of the clinical factors might be substantially less.
The importance and impact of the foregoing studies are further emphasized by a number of key strengths. First, the samples used in the studies came from a variety of academic and community hospitals and reflect a variety of practice settings and different geographical locales. Second, the training and validation sets used came from two separate clinical trials which minimized the potential for spurious trends in the data to influence the model and result in overfitting. Third, since it is unlikely that genomic profiles would be used independently from clinical risk factors in the evaluation of indeterminate pulmonary nodules, we incorporated known clinical risk factors of lung cancer or their genomic correlates directly into our classifier. Fourth, the samples were prospectively collected and cancer status was unknown at the time of collection. Finally, we showed the potential utility of sampling nasal epithelium as a faster, cheaper, and non-invasive alternative to sampling bronchial epithelium which can be easily be obtained to evaluate patients with suspect lung cancer.
Together, the findings demonstrate the existence of a cancer-associated airway field of injury that can be non-invasively sampled using nasal epithelium and that nasal gene expression harbors unique information about the presence of cancer that is independent of standard clinical risk factors. These findings, in particular the high NPV of nasal clinicogenomic biomarker, suggest that nasal epithelial gene expression can potentially be used in lung cancer detection and may be especially useful in the management of indeterminate pulmonary nodules.
Patients were enrolled at 28 medical centers in the US, Canada and Europe as part of two prospective observational studies within the Airway Epithelium Gene Expression in the Diagnosis of Lung Cancer (AEGIS) clinical trials (registered as NCT01309087 and NCT00746759). Inclusion and exclusion criteria have been previously described (Silvestri, et al. NEJM 2015). All patients were current or former cigarette smokers (defined as having smoked at least 100 cigarettes in their lifetime) undergoing bronchoscopy as part of their diagnostic workup for clinical suspicion of lung cancer and all samples were collected prospectively prior to diagnosis. The diagnosis of cancer/no cancer in this cohort has been previously described (Silvestri, et al. NEJM 2015). From among the 1067 nasal samples collected in AEGIS-1 and AEGIS-2, we selected 554 samples for initial inclusion in this study based on RNA yield and sample quality.
Nasal epithelial cells were collected by brushing the lateral aspect of the inferior turbinate with a single sterile cytology brush. Brushings were immediately placed into an RNA preservative (Qiagen RNAProtect, Cat. 76526). Nasal epithelial cells were processed to isolate RNA using Qiagen miRNeasy Mini Kits (Cat. 217004) as per the manufacturer's protocol. RNA concentration and purity were quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific) and RNA integrity (RIN) was assessed using the 2100 Bioanalyzer (Agilent Technologies). All samples were subsequently stored at −80° C. until processing on microarrays.
All procedures were performed as described in the GeneChip® Whole Transcript Sense Target Labeling Assay Manual (Affymetrix, Santa Clara, Calif.) and Ambion® WT Expression Kit Protocol (Life Technologies). In vitro transcription and cDNA fragmentation quality controls were carried out by running an mRNA Nano assay in the Agilent 2100 Bioanalyzer. The labeled fragmented DNA was hybridized to Affymetrix Gene 1.0 ST microarrays. The hybridized samples were washed and stained using Affymetrix fluidics. Microarrays were immediately scanned using Affymetrix GeneArray Scanner 3000 7G Plus (Affymetrix, Santa Clara, Calif.). The technical quality of the data from each sample was assessed using multiple quality metrics as described herein. Any sample that failed to achieve minimally acceptable thresholds for >3 quality metrics were excluded from further analysis. CEL files from all patient samples passing quality control were normalized using the Robust Multichip Average (RMA) algorithm (Irizzari, et al., Biostatistics 2003) and the Chip Definition File for the Affymetrix Gene 1.0 ST array provided by Affymetrix. Nasal and bronchial samples were normalized separately. ComBat (Johnson, et al., Biostats 2007) was used within each dataset to correct for microarray-processing batch effects. No covariates were included in the ComB at model.
Genes associated with cancer status in nasal epithelium were identified using empirical Bayes linear models (Smyth, SAGMB 2004) that corrected for smoking status, pack years, gender, age, and RIN. The most differentially expressed genes (p<0.001, n=535) were clustered using consensus hierarchical clustering (Monti et al., Machine Learning 2003, Wilkerson, et al. Bioinformatics 2010) with Pearson distance and Ward linkage. The sample dendrogram was cut to yield two groups of samples. The difference in the proportion of cancer samples to benign samples in each group was tested using a Pearson's Chi-squared test for count data. The optimal number of gene clusters was determined using the delta-area under the Cumulative Distribution Function curve as described by Monti et al. The mean of each cluster was computed and its association with cancer status was assessed using a Welch t-test. The functional enrichment of the genes in each cluster was determined using the web-based tool EnrichR (Chen, et al. 2013 BMC Bioinfo). A manual review of the literature was used to summarize the significant enrichments within each cluster into an overall cluster theme.
Gene Set Enrichment Analysis (GSEA) (Subramanian, et al. PNAS 2005) was used to determine if the genes with cancer-associated expression in nasal epithelium were concordantly enriched among the genes with cancer-associated expression in the bronchial epithelium. Briefly, the most differentially expressed genes were segregated into up-regulated and down-regulated gene sets. In bronchial epithelium samples, each gene's association with binary cancer status (1/0) was assessed using a Welch t-test. Moderated (empirical Bayes) t-statistics were computed for each gene and genes were subsequently ranked by t-statistic in the bronchial data in descending order. The pre-ranked function within the GSEA software package was then used to determine the enrichment of the two nasal gene sets among the top and bottom ranked genes in bronchial samples. Normalized enrichment scores, p-values, and FDR values were calculated using the GSEA software tool (Subramanian, et al. PNAS 2005). Genes on the leading edge of each enrichment plot (core enrichment) were identified based on the GSEA enrichment report. These genes were clustered in nasal samples using unsupervised hierarchical clustering with Ward linkage. Similar to the approach delineated above, the sample dendrogram was cut to yield two groups of samples and Pearson's Chi-squared test for count data was used to test the difference in the proportion of cancer samples to benign samples in each group.
Projection of Bronchial Clusters into Nasal Training Set
Eleven gene clusters previously identified as being associated with cancer in the bronchial epithelium (Whitney, et al. BMC Med Gen 2015) were projected into our nasal training set by taking the mean of the cluster genes per sample. The number of genes per cluster ranged from 1 to 47. The correlation of cluster means between matched bronchial and nasal samples was computed using Pearson's method. The association of each cluster mean with the presence or absence of cancer was computed using a Welch t-test.
The bronchial genomic lung cancer classifier was implemented as previously described (Whitney, et al. BMC Med Gen 2015). The present inventors computed the classifier score for each of the bronchial and nasal samples from the AEGIS-1 clinical trial. After applying a mean-shift to the nasal data as previously described (Whitney, et al. BMC Med Gen 2015) and detailed below, the classifier score was computed for each nasal sample in the AEGIS-1 trial (n=375). The correlation of the classifier score between matched bronchial and nasal samples from the AEGIS-1 trial (n=157) was computed using Pearson's product-moment coefficient.
The 535 genes whose expression was associated with cancer status made up the initial pool of candidate genes for the lung cancer classifier. Weighted voting was chosen as the classification algorithm because of its proven utility in similar classification problems (Spira, et al. Nat Med 2007). The optimal number of genes for the classifier was determined using 100 random 80/20 splits of the training set. The number of genes that maximized the average AUC across the 100 iterations was used. The genes included in the final model were selected for, and the classifier trained, using the entire training set. Details regarding the cross-validation and gene selection processes are further described below.
Gene expression surrogates for smoking status (current/former) and time since quit (<15y, ≥15y) were derived as follows. Specifically, empirical Bayes t-tests were used to identify genes that were significantly associated with each variable. The top 10 most up-regulated and top 10 most down-regulated genes by t-statistic were initially selected, followed by a down-selection of genes using forward selection and the lasso in cross-validation. Methodological details regarding this procedure are outlined herein. The set of genes that maximized the average cross-validation AUC while minimizing the total number of genes in the model were included in the genomic correlate. Finally, a logistic regression model was trained to predict the variable using the selected genes.
A clinical risk factor classifier was derived using logistic regression in the training set. This model included the genomic smoking status and time since quit classifier scores as well as age and mass size (<3 cm, ≥3 cm, infiltrates). A clinicogenomic classifier was derived in the training set using cross-validation. A penalized logistic regression model with cancer status as the dependent variable was derived using the penalized R package. Unpenalized independent variables in the model included the smoking status and time since quit genomic correlate prediction scores, patient age, and mass size. The cancer gene expression classifier prediction score was included as the only penalized independent variable in the model.
Statistical differences in clinical covariates between patients with and without lung cancer were calculated using Fisher's exact test (categorical variables) or Welch t-test (continuous variables). Differential expression analyses were performed using linear modeling (limma R package) or Welch t-tests unless otherwise specified. For the differential expression analysis, a two-sided P value of less than 0.001 was considered evidence of statistically significant differential expression. Correlation coefficients were calculated using Pearson's product-moment coefficient. Accuracy of each model was assessed using standard measures including ROC curve AUC, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Differences between receiver operating characteristic (ROC) curve AUC were assessed using DeLong's test (DeLong, et al. Biometrics 1988) for correlated ROC curves. Operating points for binary classification were chosen as the threshold that maximized sensitivity while maintaining 50% specificity in the training set. Differences in sensitivity and specificity between models were assessed using McNemar's chi-squared test for count data (Agresti, Cat. Data Analysis 1990). Statistical differences in NPV between models were assessed using the generalized score statistic (Leisenring W, et al., Biometrics 2000) for paired analyses or a proportions test for unpaired analyses. All confidence intervals (CIs) were reported as two-sided binomial 95% confidence intervals. All statistical tests were two-sided, and a P value of less than 0.05 was considered statistically significant.
All samples used in this study were obtained from patients with suspect lung cancer enrolled in the AEGIS-1 and AEGIS-2 clinical trials. By nature of the inclusion criteria, these clinical trials were enriched for patients that were ultimately diagnosed with lung cancer. As a result, the present investigators were limited by the number of samples we could select that had a benign diagnosis at 1 year follow up. The inventors selected all benign samples with sufficient RNA yield after isolation and then selected cancer samples to match the clinical covariates of the benign group. As a result, the cancer and benign classes are very well balanced for the recorded clinical covariates (Table 1).
All analytical methods were carried out using the R statistical computing environment. The quality of each microarray CEL file was assessed using the ArrayQualityMetrics R package (Kauffmann, et al. Bioinformatics 2009). Nine metrics were computed per CEL file (6 prior to RMA normalization, and 3 post-RMA normalization and batch correction). See Table 11 for a description of each quality metric and associated thresholds used to evaluate CEL files. Samples failing at least three quality metrics were removed from all subsequent analyses.
For differential expression analyses in nasal samples, only probesets that were expressed in at least 5% of samples were included to reduce noise and data dimensionality. Background-level expression was determined by examining the expression level of Y-chromosome genes DDX3Y, KDM5D, RPS4Y1, and USP9Y represented by probesets 8176375, 8176578, 8176624, 8177232 in female samples from the training set. Probesets whose expression level did not exceed 1.5 positive standard deviations of the mean expression of the four Y-linked genes in at least 5% of samples were not considered in the analyses.
To account for the difference in gene expression intensity between bronchial and nasal tissues, the present inventors performed a gene-wise mean-shift which was estimated using nasal samples that had a matched bronchial sample in the training set in which the bronchial classifier was developed (n=157) (Whitney, et al. BMC Med Gen 2015). Specifically, the mean expression of each gene in nasal samples (n=157) was subtracted from its corresponding mean expression in bronchial samples. The difference was then added to that gene's expression level in all nasal samples. The bronchial genomic classifier was then evaluated on the mean-shifted nasal data.
The training set was randomly divided with 80% of samples belonging to an internal training set and the remaining 20% of samples belonging to an internal test set. Within each split of the data, the association of each gene's expression with cancer status was assessed using Student's t-test. The genes were ranked by absolute t-statistic and a varying number of the top-ranked genes were selected for inclusion in the weighted voting classifier. Classifiers composed of 5 to 100 genes were considered. The performance of each internally trained classifier was quantified using the AUC in the internal test set. This cross-validation procedure was repeated for 100 iterations. The AUC values across the 100 splits of the data were used to rank the models. The classifier size that maximized average cross-validation AUC while minimizing standard deviation and minimizing the number of genes in the classifier was selected as optimal. The genes included in this model were selected for using the entire training set. The final weighted voting classifier was trained using the entire training set and locked prior to evaluation in the validation set.
4779 genes were significantly associated with smoking status (p<0.001). Among the top 20 most differentially expressed, 5 were selected for inclusion in a logistic regression model to optimize prediction of smoking status based on cross-validation (Table 14). Specifically, the present inventors used the lasso as a feature selection algorithm to reduce the number of genes in our final model. Using the nasal training set and top 20 genes as a starting point, we fit logistic regression models with binary smoking status (current/former) as the dependent variable and the 20 genes as independent variables using the lasso. The present inventors varied the values of the shrinkage parameter lambda to calculate the misclassification error rate in 10-fold cross-validation using the cv.glmnet function in the glmnet R package (Friedman, et al. JSS 2008). With increasing values of lambda, more genes are allowed to remain in the model. The present inventors iterated over each value of lambda and recorded which genes were included in the models as lambda increased. Using these sets of genes, we fit ordinary logistic regression models in 10-fold cross-validation and computed the average test set AUC for each subset of genes. The subset that obtained the highest average AUC while minimizing the number of genes in the model was considered optimal and those genes were included in the final logistic regression model which was trained using the entire training set. This model was able to distinguish between current and former smokers with an AUC of 0.89 in the training set (p<2.2e-16, n=375).
An identical process was employed for the derivation of the time-since-quit classifier. Specifically, 235 genes were significantly associated with whether a patient had quit smoking less than or greater than 15 years prior to sample collection (p<0.001) in a subset of the training set with valid time since quit clinical annotation (n=319). Among the top 20 most differentially expressed genes, 2 were chosen for inclusion in the final logistic regression model with time since quit as the dependent variable and the two genes as independent variables. This model was trained to optimize the prediction of time since quit (<15y, ≥15y) based on the cross-validation method described above (Table 15). This model had an AUC of 0.75 in the training set (p=0.0001, n=319).
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This application is a continuation application of U.S. application Ser. No. 16/300,947, filed Nov. 12, 2018, which is a U.S. National Application of PCT/US2017/032517 filed May 12, 2017, which claims benefit of U.S. Provisional Application 62/335,391 filed May 12, 2016. The entire teachings of the above applications are incorporated herein by reference.
This invention was made with government support under Contract No. CA152751 and CA214182 awarded by the National Institutes of Health. The Government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 62335391 | May 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16300947 | Nov 2018 | US |
| Child | 17397905 | US |
