US20180057889A1 - Digital Analysis of Circulating Tumor Cells in Blood Samples - Google Patents

Digital Analysis of Circulating Tumor Cells in Blood Samples Download PDF

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US20180057889A1
US20180057889A1 US15/560,324 US201615560324A US2018057889A1 US 20180057889 A1 US20180057889 A1 US 20180057889A1 US 201615560324 A US201615560324 A US 201615560324A US 2018057889 A1 US2018057889 A1 US 2018057889A1
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cancer
ctcs
cdna
ctc
pcr
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Daniel A. Haber
Ravi Kapur
Mehmet Toner
Shyamala Maheswaran
Xin Hong
David Tomoaki Miyamoto
Tanya TODOROVA
Sarah Javaid
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General Hospital Corp
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Assigned to THE GENERAL HOSPITAL CORPORATION reassignment THE GENERAL HOSPITAL CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TODOROVA, Tanya, HABER, DANIEL A., HONG, XIN, JAVAID, Sarah, MIYAMOTO, David Tomoaki, KAPUR, RAVI, MAHESWARAN, SHYAMALA, TONER, MEHMET
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    • C12Q2600/16—Primer sets for multiplex assays

Definitions

  • This invention relates to blood sampling techniques, and more particularly to methods and systems for detecting and analyzing cells in blood samples.
  • CTCs rare circulating tumor cells
  • CTCs are very rare, and identifying, visualizing, and scoring these tumor cells admixed with normal blood components remains a significant challenge, even after partial purification with known microfluidic devices or similar technologies.
  • RBCs red blood cells
  • WBCs white blood cells
  • antibody staining of tumor cells is highly variable, due to high heterogeneity among cancer cells, even within an individual patient, as well as the poor physical condition of many tumor cells that circulate in the bloodstream, many of which have begun to undergo programmed cell death or anoikis.
  • accurate scoring of antibody-stained tumor cells requires differentiation from large numbers of contaminating white blood cells, some of which bind to antibody reagents non-specifically. As such, only a subset of candidate tumor cells can be robustly identified by antibody staining, and as many as half of patients tested have no detectable cells, despite having widely metastatic cancer.
  • the present disclosure relates to methods, uses, and systems to obtain the highest possible sensitivity of data relating to rare CTCs in standard blood samples, while avoiding the need for extremely high levels of purity of the CTCs.
  • the new methods do not need the CTCs to be completely isolated from contaminating WBCs, and instead can reliably detect as few as one CTC in products containing, e.g., up to 10,000 WBCs or more.
  • the new assay methods and systems combine (1) an isolation system that can consistently obtain CTCs as intact, whole cells (with high quality ribonucleic acid (RNA)) from blood with (2) a droplet-based digital polymerase chain reaction (PCR) assay focused on ribonucleic acid RNA markers of specific cancer lineages for each tumor type that are absent in blood of healthy patients.
  • RNA ribonucleic acid
  • the isolation system is a microfluidic system, such as a negative depletion microfluidic system (e.g., a so-called “CTC-Chip,” that uses negative depletion of hematopoietic cells, e.g., red blood cells (RBCs), WBCs, and platelets, to reveal untagged non-hematopoietic cells such as CTCs in a blood sample).
  • a negative depletion microfluidic system e.g., a so-called “CTC-Chip,” that uses negative depletion of hematopoietic cells, e.g., red blood cells (RBCs), WBCs, and platelets, to reveal untagged non-hematopoietic cells such as CTCs in a blood sample.
  • the disclosure relates to methods for early detection of cancer with ultra-high sensitivity and specificity, wherein the methods include the use of microfluidic isolation of circulating tumor cells (CTCs) and digital detection of RNA derived from the CTCs.
  • CTCs circulating tumor cells
  • RNA derived from the CTCs can be converted into cDNA and encapsulated into individual droplets for amplification in the presence of reporter groups that are configured to bind specifically to cDNA from CTCs and not to cDNA from other cells.
  • the droplets positive for reporter groups can be counted to assess the presence of disease, e.g., various types of cancer.
  • the disclosure relates to methods of analyzing circulating tumor cells (CTCs) in a blood sample.
  • the methods include or consist of isolating from the blood sample a product comprising CTCs and other cells present in blood; isolating ribonucleic acid (RNA) molecules from the product; generating cDNA molecules in solution from the isolated RNA; encapsulating cDNA molecules in individual droplets; amplifying cDNA molecules within each of the droplets in the presence of reporter groups configured to bind specifically to cDNA from CTCs and not to cDNA from other cells; detecting droplets that contain the reporter groups as an indicator of the presence of cDNA molecules from CTCs in the droplets; and analyzing CTCs in the detected droplets.
  • RNA ribonucleic acid
  • the methods described herein can further include reducing a volume of the product before isolating RNA and/or removing contaminants from the cDNA-containing solution before encapsulating the cDNA molecules.
  • generating cDNA molecules from the isolated RNA can include conducting reverse transcription (RT) polymerase chain reaction (PCR) of the isolated RNA molecules and/or amplifying cDNA molecules within each of the droplets can include conducting PCR in each droplet.
  • encapsulating individual cDNA molecules and PCR reagents in individual droplets can include forming at least 1000 droplets of a non-aqueous liquid, such as one or more fluorocarbons, hydrofluorocarbons, mineral oils, silicone oils, and hydrocarbon oils and/or one or more surfactants.
  • Each droplet can contain, on average, one target cDNA molecule obtained from a CTC.
  • the reporter groups can be or include a fluorescent label.
  • the new methods can include removing contaminants from the cDNA-containing solution by use of Solid Phase Reversible Immobilization (SPRI), e.g., immobilizing cDNA in the solution, e.g., with magnetic beads that are configured to specifically bind to the cDNA; removing contaminants from the solution; and eluting purified cDNA.
  • SPRI Solid Phase Reversible Immobilization
  • the methods described herein include using probes and primers in amplifying the cDNA molecules within each of the droplets that correspond to one or more genes selected from the list of cancer-selective genes in Table 1 herein.
  • the selected genes can include prostate cancer-selective genes, e.g., any one or more of AGR2, FOLH1, HOXDB13, KLK2, KLK3, SCHLAP1/SET4, SCHLAP1/SET5, AMACR, AR variants, UGT2B15/SET1, UGT2B15/SET5, and STEAP2 (as can be easily determined from Table 1).
  • any one or more of ALDH1A3, CDH11, EGFR, FAT1, MET, PKP3, RND3, S100A2, and STEAP2 are selective for pancreatic cancer. Similar lists can be generated for the other types of cancers listed in Table 1.
  • the selected genes include any one or more of the breast cancer-selective genes listed in Table 1.
  • the selected genes include genes selective for one or more of lung, liver, prostate, pancreatic, and melanoma cancer.
  • a multiplexed assay can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or even all of the selected genes that are listed in Table 1 as being selective for a particular type of cancer, e.g., breast cancer, lung cancer, prostate cancer, pancreatic cancer, liver cancer, and melanoma.
  • a group of primers and probes for 5 to 12 cancer-selective genes from Table 1 are used for a particular type of cancer.
  • Other specific combinations of selected genes are described in the Examples below.
  • the methods can also include using one or more genes selective for two or more, three or more, four or more, or five or more different types of cancer.
  • the genes can be selective for breast cancer and lung cancer; breast cancer, lung cancer, and liver cancer; breast cancer, lung cancer, and pancreatic cancer; breast cancer, lung cancer, and prostate cancer; breast cancer, liver cancer, and melanoma; breast cancer, lung cancer, and melanoma; breast cancer, lung cancer, liver cancer, and prostate cancer; breast cancer, lung cancer, liver cancer, and melanoma; breast cancer, lung cancer, liver cancer, and pancreatic cancer; breast cancer, lung cancer, prostate cancer, and pancreatic cancer; breast cancer, lung cancer, liver cancer, melanoma, and pancreatic cancer; or breast cancer, lung cancer, liver cancer, melanoma, pancreatic cancer, and prostate cancer.
  • the CTCs can arise from metastatic or primary/localized cancers.
  • the step of analyzing the CTCs in the detected droplets cam include monitoring CTCs from a blood sample from a patient, e.g., with a known cancer, e.g., over time, and testing and/or imaging the CTCs (e.g., using standard techniques) to provide a prognosis for the patient.
  • the step of analyzing the CTCs in the detected droplets can include testing and/or imaging the CTCs (e.g., using standard techniques) from a blood sample from a patient to provide an indication of a response by the CTCs to a therapeutic intervention.
  • the step of analyzing the CTCs in the detected droplets includes determining a number or level of CTCs per unit volume of a blood sample from a patient to provide a measure of tumor burden in the patient.
  • the methods can then further include using the measure of tumor burden in the patient to select a therapy or can further include determining the measure of tumor burden in the patient at a second time point to monitor the tumor burden over time, e.g., in response to a therapeutic intervention, e.g., for dynamic monitoring of tumor burden.
  • the methods and assays described herein can be used to amplify and detect CTCs in a wide variety of diagnostic, prognostic, and theranostic methods.
  • CTCs circulating tumor cells
  • solid tumors non-hematogenous cancers
  • WBCs circulating tumor cells
  • a “product” means a group of isolated rare cells and other contaminating blood cells, e.g., red blood cells, white blood cells (e.g., leukocytes), e.g., in some sort of liquid, e.g., a buffer, such as a pluronic buffer, that arise from processing in the methods described herein, e.g., using the systems described herein.
  • a typical product may contain only about one to ten CTCs admixed with 500 to 2,500 or more WBCs, e.g., one to ten CTCs in a mixture of 1000 to 2000 WBCs.
  • the limit of detection of the present methods can be about 1 CTC in 10,000 WBC.
  • the present methods can achieve a level of purity of about 1 CTC in 500 WBCs, the present methods do not require highly purified CTCs, as is required in some known methods of CTC analysis.
  • Solid Phase Reversible Immobilization (SPRI) cleanup is a technique using coated magnetic beads to perform size selection on cDNA created from Reverse Transcription (RT)-PCR of a product.
  • SPRI Solid Phase Reversible Immobilization
  • PCR polymerase chain reaction
  • RT-PCR refers to the use of reverse transcription to generate a complementary c-DNA molecule from an RNA template, thereby enabling the DNA polymerase chain reaction to operate on RNA.
  • An important aspect of the new methods disclosed herein is the availability of high quality RNA from whole cell CTCs that are not lysed or treated in such a way that might destroy or degrade the RNA.
  • positive droplets are lipid-encapsulated molecules in which a PCR reaction performed with tagged primers allows visualization of the PCR amplified product.
  • a droplet that contained a single template cDNA molecule of a particular targeted gene can become visible using fluorescence microscopy, while an “empty” or “negative” droplet is one that contains no targeted cDNA.
  • the new methods and systems provide numerous advantages and benefits. For example, the current methods and systems provide results that are far more accurate and robust than either of the prior known systems when used alone.
  • the new digital-CTC assays enable dramatic signal amplification.
  • the background signal from normal blood cells is negligible in d-CTC.
  • d-CTC enables greatly amplified signal from patients with advanced cancer (nearly 100% of patients with prostate, lung, breast, and liver cancers).
  • the signal amplification permits detection of CTC-derived signatures even in patients with a very low tumor burden (something that is not readily accomplished with CTC cell imaging), thus enabling significantly earlier detection of cancer.
  • this novel microfluidics platform provides a streamlined, ultrahigh-throughput, rapid (e.g., 3 hours per run), and extremely high sensitivity method of enriching, detecting, and analyzing CTCs in patient blood samples.
  • the platform provides rich, clinically actionable information, and with further optimization may enable early detection of cancer.
  • FIG. lA is a graph showing cDNA dilutions prepared from total RNA of LNCaP prostate cancer cells, mixed with leukocytes and analyzed by droplet PCR using two different prostate primer sets. The results represent several purities and show good response of positive droplet number across this range.
  • FIG. 1B is a graph of manually isolated LNCaP cells spiked into healthy donor (HD) blood samples, run through the CTC-iChip, and subjected to droplet RT-PCR (KLK3 primer set). The results show excellent sensitivity down to low numbers of target cells.
  • FIG. 1C is a graph that shows the analysis of blood samples from healthy controls, patients with localized (resectable) prostate cancer and metastatic prostate cancer, processed through the CTC-iChip, subjected to RT-PCR and droplet analysis using three prostate-specific and one epithelial-specific biomarkers (KLK3, AMACR, FOLH1, EpCAM). The results are shown for the total number of droplets/ml for all four markers combined.
  • FIG. 2 is a signal intensity plot that shows KLK3 positive droplets derived from LNCAP prostate cancer cells spiked into blood and recovered using the CTC-iChip.
  • FIG. 3 is a bar graph that shows the minimal variation between experimental replicates and the retention of signal after sample processing through the CTC-iChip and shows increased detection sensitivity using the new assays described herein.
  • FIG. 4 is a signal intensity plot that shows the absence of four different cancer-specific marker-positive droplets in healthy donors using the new CTC digital droplet PCR assay methods described here (“CTC ddPCR” assay or simply “d-CTC” assay).
  • FIG. 5 is a signal intensity plot that shows a d-CTC assay multiplexed for four different lineage specific transcripts to detect prostate cancer cell lines spiked into blood.
  • FIGS. 6A to 7B are signal intensity plots showing d-CTC assays multiplexed for four different prostate cancer-specific transcripts per reaction. Both the theoretical model ( FIGS. 6A and 7A ) and cancer cell line data ( FIGS. 6B and 7B ) shown for two such reactions, Reactions 1 and 2, demonstrate that the theoretical model accurately predicts the experimental data.
  • FIGS. 8A to 13B are signal intensity plots showing d-CTC assays multiplexed for four different breast and lung cancer specific transcripts per reaction.
  • FIG. 14 is a bar graph showing droplet PCR signal for seven different biomarkers (PIP, PRAME, RND3, PKP3, FAT1, S100A2, and AGR2) from 1 ng of non-amplified cell-line cDNA and from 1 ⁇ l of pre-amplified product after 10, 14, and 18 cycles of Specific Target Amplification (STA) pre-amplification, demonstrating the significant enhancement of droplet PCR signal from STA pre-amplification.
  • biomarkers PIP, PRAME, RND3, PKP3, FAT1, S100A2, and AGR2
  • FIGS. 15A to 15C are graphs that show the results of CTC detection in patients using the new d-CTC assay methods for three different sets of patients with lung cancer ( FIG. 15A ), breast cancer ( FIG. 15B ), and prostate cancer ( FIG. 15C ). In each, the healthy patients had no CTCs.
  • FIG. 16 is a horizontal bar graph that shows the results of patient prostate cancer data using a multiplexed d-CTC assay method described herein testing for the nine biomarkers recited in the figure (AGR2, Dual, FAT1, FOLH1, HOXB13, KLK2, KLK3, STEAP2, and TMPRSS2). 91 percent of cancer patients had detectable CTCs (10 of 11 patients), 24 of 28 samples contained detectable CTCs (86%), and 0 of 12 (0 percent) of healthy donor (HD) blood samples contained CTCs.
  • AGR2 Dual, FAT1, FOLH1, HOXB13, KLK2, KLK3, STEAP2, and TMPRSS2
  • 91 percent of cancer patients had detectable CTCs (10 of 11 patients), 24 of 28 samples contained detectable CTCs (86%), and 0 of 12 (0 percent) of healthy donor (HD) blood samples contained CTCs.
  • FIG. 17 is a series of signal intensity plots showing d-CTC assays multiplexed for for two different reactions (Reaction 1 (TMPRSS2, FAT1, KLK2, and STEAP2), left column, and Reaction 2 (KLK3, HOXB13, AGR2, and FOLH1), right column) for blood samples from a metastatic prostate cancer patient (top row), a localized prostate cancer patient (middle row), and from a healthy donor control sample (bottom row). In each case there were no CTCs in the healthy donor (HD) samples, but clear evidence of CTCs in the cancer samples.
  • Reaction 1 TMPRSS2, FAT1, KLK2, and STEAP2
  • KLK3, HOXB13, AGR2, and FOLH1 Reaction 2
  • FIG. 18 is a multiple bar graph illustrating the relative proportion of androgen receptor signaling genes in CTCs measured over time to provide a readout of drug response in a prostate cancer patient treated with Abiraterone®.
  • FIGS. 19A and 19B are graphs showing non-amplification versus 18 cycles of SMARTer pre-amplication.
  • FIG. 19A is a bar graph that shows the level of amplicon amplification efficiency for different target regions that is consistent among the three replicates (WTA1, WTA2, WTA3).
  • FIG. 19B is a graph that shows that using 18 cycles of SMARTer pre-amplification provides an increase in signal of approximately four orders of magnitude (10 8 vs 10 4 ) compared to a non-pre-amplified sample.
  • FIGS. 20A to 20C are graphs that show the results of testing of 11 markers in a multiplexed liver cancer assay.
  • FIGS. 20A to 20C show the total droplet numbers in 21 hepatocellular carcinoma (HCC) patients ( FIG. 20A ), 13 chronic liver disease (CLD) patients ( FIG. 20B , no significant detectable droplets) and 15 healthy donors (HDs) ( FIG. 20C , no significant detectable droplets).
  • HCC hepatocellular carcinoma
  • CLD chronic liver disease
  • HDs healthy donors
  • FIGS. 21A and 21B are graphs that show the results of a 14 marker multiplexed lung cancer assay.
  • FIG. 21A shows the assay results for the 8 metastatic lung cancer patients and 8 healthy donors (all negative).
  • FIG. 21B shows that all of the droplet counts per ml of blood in the cancer patients (8 of 8) were higher than in all healthy donors giving a detection rate of 100% in this assay.
  • FIG. 22 is a graph that shows the results of a breast cancer assay for a multiplexed eleven marker assay used in a field of 9 metastatic breast cancer patient, 5 localized breast cancer patients, and 15 healthy donors. The results show that the assay detects cancer in 7 of 9 metastatic breast cancer patients, 2 of 5 localized breast cancer patients, and none of the healthy donor samples.
  • FIGS. 23A and 23B are graphs that show the results of ARv7 detection in metastatic breast cancer patients.
  • FIG. 23A is a bar graphs that shows the results for samples from 10 metastatic breast cancer patients and 7 healthy donors processed though the CTC-Chip as described herein.
  • FIG. 23B shows that five of the ten cancer patient samples were above the healthy donor background level giving a detection rate of 5 in 10, or 50%.
  • FIG. 24A is a bar graph showing the detection rate of individual markers (PMEL, MLANA, MAGEA6, PRAME, TFAP2C, and SOX10) and a combined marker cocktail (SUM) in 34 melanoma patients.
  • FIG. 24B is a dot plot distribution of droplet signals detected in 34 melanoma patients for 182 draw points as compared to 15 healthy donors demonstrating an overall detection sensitivity above healthy donor background signal of 81% (based on draw points) and a specificity of 100% (by draw points).
  • the present disclosure relates to methods and systems to obtain information from rare cancer cells in blood samples.
  • isolation techniques such as ultrahigh-throughput microfluidic techniques, for example, negative depletion techniques, e.g., those using negative depletion of hematopoietic cells to isolate untagged CTCs in a blood sample
  • analysis techniques such as droplet-based digital polymerase chain reaction (PCR) assays focused on ribonucleic acid (RNA) markers of specific cancer lineages.
  • PCR digital polymerase chain reaction
  • RNA ribonucleic acid
  • This strategy can also be applied to other CTC isolation technologies that provide partially purification of cells (e.g., filtration, positive tumor cell selection), although the quality of the RNA and hence the sensitivity of the assay will be inferior to the microfluidic technologies.
  • other digital PCR technologies applied to RNA are capable of detecting lineage-specific primers, although the sensitivity of the droplet-based assay is likely to be the highest.
  • the new methods described herein can be used not only for early detection of cancers based on the presence of the CTCs in the blood, but also for tumor burden quantification as well as to monitor CTCs from a particular tumor over time, e.g., to determine any potential changes in specific tumor marker genes present in the CTCs as well changes in the tumor as the result of specific therapies, e.g., in the context of a clinical trial or a particular therapy.
  • the isolation techniques are used to enrich CTCs from a blood sample, e.g., using ultrahigh-throughput microfluidic such as the so-called “CTC-iChip” described in, for example, International PCT Application WO 2015/058206 and in Ozkumur et al., “Inertial Focusing for Tumor Antigen-Dependent and -Independent Sorting of Rare Circulating Tumor Cells,” Sci. Transl. Med., 5:179ra47 (2013).
  • the CTC-iChip uses a CTC antigen-independent approach in which WBCs in the blood sample are labeled with magnetic beads, and the sample is then processed through two enrichment stages.
  • the first stage uses deterministic lateral displacement to remove small and flexible cells/particles (RBCs, platelets, unbound magnetic beads, and plasma) while retaining larger cells (CTCs and WBCs).
  • the second stage moves all cells into a narrow fluid stream using inertial focusing and then uses a magnetic field to pull bead-labeled WBCs out of the focused stream, leaving highly enriched CTCs.
  • the CTC-iChip product from 10 ml of whole blood typically contains ⁇ 500,000 RBCs, ⁇ 5,000 WBCs, and a variable number of CTCs.
  • Some analysis techniques further enrich and analyze the isolated CTCs, e.g., as obtained from the CTC-iChip, e.g., using droplet microfluidics.
  • Some basic information on droplet microfluidics is described generally in Jeremy et al., “Ultrahigh-Throughput Screening in Drop-Based Microfluidics for Directed Evolution,” Proc. Natl. Acad. Sci. USA, 107:4004 (2010).
  • the droplet microfluidic techniques can, in certain implementations, include encapsulation of single cells, RT-PCR reagents, and lysis buffer into droplets of typically non-aqueous liquids (e.g., fluorocarbons, hydrofluorocarbons, mineral oil, silicone oil, and hydrocarbon oil; surfactants can also be include in the non-aqueous liquid, e.g., Span80, Monolein/oleic acid, Tween20/80, SDS, n-butanol, ABIL EM90, and phospholipids), in the size range of, e.g., about 0.5 pL to 15 nL in volume and, e.g., 10 to 300 ⁇ m, e.g., 20 to 100 ⁇ m, e.g., 30 to 50 ⁇ m, e.g., 35 ⁇ m in diameter.
  • typically non-aqueous liquids e.g., fluorocarbons, hydrofluorocarbons, mineral
  • these techniques further include amplification of cancer-specific transcripts within the droplets to produce a fluorescent signal, and sorting of amplification-positive drops.
  • This approach results in isolation of pure CTCs that can be sequenced and analyzed for the purposes of diagnosis and individualized drug therapy. Due to the high heterogeneity of CTCs, it is useful to use multiplexed amplification to detect as many CTCs as possible. Thus, instead of using one pair of primers in the PCR mixture, one can increase the probability of detecting and sorting CTCs using a combination of tumor specific primers.
  • RNA molecules are representative of the genes expressed in a cancer cell. Most are “lineage” specific, rather than cancer specific, for example any prostate cell (whether cancerous or not) expresses these markers. However, normal blood cells do not, and the fact that the signal is derived from a cell circulating in the bloodstream defines it as an abnormal signal.
  • PCR amplify this lineage signal.
  • droplet digital PCR which is extraordinarily sensitive, allowing to convert the signal from a single cancer cell (i.e., one signal in an imaging assay) into thousands of positive immunofluorescent droplets.
  • the combination of multiple, highly curated gene transcripts ensures high sensitivity and specificity for cancer, and also allows for functional insights (as in the status of hormone responsive pathways in prostate and breast cancers).
  • RNA markers As noted, the new assay methods focus on the detection and analysis of high quality RNA rather than DNA. While there has been considerable work on DNA mutation detection in plasma and in CTCs, the present methods rely on RNA markers for the following reasons:
  • RNA mutations are not tumor specific, and the discovery that a healthy individual has some unidentified cancer cells in the blood is a very difficult clinical situation. In contrast, by selecting tumor-specific RNAs (e.g., prostate vs lung), the new methods can identify the source of cancer cells in the blood.
  • tumor-specific RNAs e.g., prostate vs lung
  • DNA mutations are very heterogeneous and besides a few recurrent mutations shared by many cancers, most blood-based mutation detection strategies require pre-existing knowledge of the mutations present in the primary tumor (i.e. not appropriate for screening for unknown cancers). In contrast, all tumor cells derived from specific organs express common lineage markers at the RNA level. Thus, a single cocktail of markers is used in the new methods for each individual type of cancer.
  • RNA is extraordinarily sensitive. However, free RNA is degraded in the bloodstream, and the use of isolation systems as described herein, such as microfluidic negative depletion systems (e.g., the CTC-Chip system) is unique in that the untagged tumor cells have high quality RNA which is extractable.
  • isolation systems as described herein, such as microfluidic negative depletion systems (e.g., the CTC-Chip system) is unique in that the untagged tumor cells have high quality RNA which is extractable.
  • cDNA as a target molecule over DNA was made to not only to boost the signal originating from each tumor cell, but also to specifically target only tumor cell transcripts to the exclusion of white blood cell (WBC) transcripts.
  • WBC white blood cell
  • the boost in signal is a significant advantage, as it avoids the need for the isolation of CTCs to very high levels of purity. That is, it enables robust and repeatable results with products that contain one or more “isolated” CTCs that are still surrounded by hundreds or thousands of contaminating WBCs, e.g., leukocytes, in the same product.
  • the strategy of targeting cDNA made from RNA as used in the new methods allows the new assay methods to beakily tailored for maximum specificity with minimal levels of CTC purity compared to prior approaches.
  • the CTC-iChip technology is highly efficient at isolating non-hematopoietic cells by microfluidic depletion of antibody tagged leukocytes.
  • This feature of the CTC-iChip provides intact tumor-derived RNA (at levels far above those obtained using other technologies), and it is independent of tumor cell surface epitopes (which are highly heterogeneous among cancers and among epithelial vs mesenchymal cell subtypes within an individual cancer).
  • tumor cell surface epitopes which are highly heterogeneous among cancers and among epithelial vs mesenchymal cell subtypes within an individual cancer.
  • pre-apoptotic cancer cells whose antibody staining and selection is suboptimal for imaging analysis can provide a source of tumor-specific RNA that can be scored using the methods described herein.
  • an isolation technology or system that provides high quality RNA from intact CTCs with at least some reduction in the WBCs found in the sample along with the rare CTCs, such as a microfluidic negative depletion system, e.g., the CTC-iChip, is an important first step isolation before the tumor-specific digital readout is applied to the product.
  • the droplet-based digital detection of extremely rare molecules within a heterogeneous mixture was originally developed for PCR amplification of individual DNA molecules that are below detection levels when present within a heterogeneous mixture, but which are readily identified when sequestered within a lipid droplet before being subjected to PCR.
  • the basic technology for droplet-based digital PCR (“Droplet Digital PCR (ddPCR)”) has been commercialized by RainDance and Bio-Rad, which provide equipment for lipid encapsulation of target molecules followed by PCR analysis. Important scientific advances that made this possible include work in the laboratory of David Weitz at Harvard and Bert Vogelstein at Johns Hopkins. For example, see U.S. Pat. Nos. 6,767,512; 7,074,367; 8,535,889; 8,841,071; 9,074,242; and U.S. Published Application No. 2014/0303005. See also U.S. Pat. No. 9,068,181.
  • droplet digital PCR itself is not biologically significant unless coupled to a biological source of material, which is key to the new methods described herein.
  • detection of lineage-specific RNAs does not distinguish between normal prostate epithelial cells and cancerous prostate cells.
  • detection of prostate-derived transcripts in the blood is not meaningful: they are present within debris from normal prostate cells or exosomes. It is only when coupled with the isolation of whole CTCs (i.e., intact CTCs in the blood) that the ddPCR assay achieves both extraordinary sensitivity and specificity.
  • these two technologies are ideally suited for each other, because the isolation systems provide high quality RNA, and the droplet-based digital PCR assays are focused on RNA markers in the new methods.
  • the new assay methods described herein use cDNA made from total RNA, but key to this use is the identification of appropriate biomarkers that are tumor lineage-specific for each type cancer, yet are so unique as to be completely absent in normal blood cells (even with ddPCR sensitivity).
  • the selection, testing, and validation of the multiple target RNA biomarkers for each type of cancer described herein enable the success of the new assay methods.
  • the new assay methods start with the isolation of partially pure CTCs using an isolation system, such as a microfluidic negative depletion system, up to and including the analysis of data from a droplet digital PCR instrument.
  • an isolation system such as a microfluidic negative depletion system
  • RNA ribonucleic acid
  • pre-amplifying the cDNA using gene-specific targeted preamplification probes e.g., using the Fluidigm BioMarkTM Nested PCR approach, or non-specific whole-transcriptome amplification, e.g., using the Clontech SMARTerTM approach (optional);
  • analyzing CTCs in the detected droplets e.g., to determine the presence of a particular disease in a patient or subject.
  • target gene biomarkers and corresponding primers
  • a unique list of target gene biomarkers described herein was determined using bioinformatics analyses of publicly available datasets and proprietary RNAseq CTC data. Great care was taken to select markers that are not expressed in any subpopulations of leukocytes, but are expressed at a high enough frequency and intensity in CTCs to provide a reliable signal in a reasonably wide array of different and distinct patients.
  • a specific set of markers was selected for each cancer type (e.g. prostate cancer, breast cancer, melanoma, lung cancer, pancreatic cancer, among others.)
  • CTC-iChip e.g., version 1.3M or 1.4.5 T and a sample is collected in a 15 mL conical tube on ice.
  • CTC-iChips were designed and fabricated as previously described (Ozkumur et al., “Inertial Focusing for Tumor Antigen-Dependent and -Independent Sorting of Rare Circulating Tumor Cells,” Science Translational Medicine, 5(179):179ra47 (DOI: 10.1126/scitranslmed.3005616) (2013)).
  • the blood samples ( ⁇ 20 mls per cancer patient) are collected in EDTA tubes using approved protocols. These samples are then incubated with biotinylated antibodies against CD45 (R&D Systems) and CD66b (AbD Serotec, biotinylated in house) and followed by incubation with Dynabeads® MyOne® Streptavidin T1 (Invitrogen) to achieve magnetic labeling of white blood cells (Ozkumur et al., 2013).
  • biotinylated antibodies against CD45 R&D Systems
  • CD66b AbD Serotec, biotinylated in house
  • Dynabeads® MyOne® Streptavidin T1 Invitrogen
  • the sample is then processed through the CTC-iChip, which separates the blood components (red and white blood cells and platelets) as well as unconjugated beads away from the CTCs.
  • the CTCs are collected in solution while the red blood cells, platelets, unconjugated beads and the tagged white blood cells are collected in a waste chamber.
  • the process is automated and 10 ml of blood is processed in 1 hour.
  • RNAlaterTM ThermoFisher
  • the RNA isolation step is important to the process to fully release all RNA molecules from cells in preparation for RT-PCR.
  • a one-step, in-tube reaction can be used to minimize the risk of cell and RNA loss likely to be incurred during standard transfer steps.
  • cDNA can be synthesized using the SMARTerTM Ultra Low Input RNA Kit protocol, which uses proprietary oligonucleotides and reverse transcriptase enzyme.
  • Another useful, yet optional, step in the process involves the removal of lysis reagents from the cDNA-containing solution.
  • the presence of harsh detergents can lead to the destabilization of the droplets used in the ddPCR method, once the cDNA-containing solution is transferred to the ddPCR instrument.
  • Detergent removal can be accomplished, e.g., through the use of Solid Phase Reversible Immobilization (SPRI).
  • SPRI Solid Phase Reversible Immobilization
  • This technique uses coated magnetic beads to first bind cDNA of a specific size range, then allows removal of detergent-containing supernatant, and finally elution of pure cDNA for input into the ddPCR instrument.
  • the SPRI process also accomplishes a size selection of cDNA, which reduces the number of non-target cDNA molecules that enter the ddPCR phase of the process, which in turn reduces background and noise.
  • Pre-amplification of the cDNA is an optional step that increases the number of template molecules that can be detected in the droplet PCR step thus improving signal-to-noise ratio and boosting the confidence in a positive read-out. It can be a very powerful approach for the detection of markers that are expressed at low levels in CTCs, and for analyzing samples that contain very small numbers of possibly apoptotic CTCs, such as in the context of early detection of pre-metastatic disease. These two approaches have been modified to be applied in the workflow of d-CTC assay.
  • STA Specific Targeted Amplification
  • SMARTerTM Ultra Low Input RNA Kit relies on the amplification of every transcript in the product, including both those found in WBCs and those found in CTCs, using random primers.
  • a droplet making instrument e.g., a droplet generator such as the Biorad Automated Droplet Generator, which generates 20,000 droplets per sample.
  • a droplet generator such as the Biorad Automated Droplet Generator
  • Each reaction consists of an extremely small droplet of non-aqueous fluid, e.g., oil (PCR stable, e.g., proprietary formulation from vendor), which contains Taqman-type PCR reagents with gene-specific primers and an oligonucleotide probe, and a small amount of sample.
  • the sample consists of an emulsion containing a vast number of individual PCR-ready reactions.
  • PCR probes and related primers for any one or two or more different target genes listed in Table 1 below for overall determination of tumor load, e.g., to determine tumor progression or response to therapy, in single or multiplex reactions.
  • Table 1 a single set of PCR primers and probes for a particular gene from Table 1 can be included in each droplet, it is also possible to multiplex PCR primers and probes for two or more different genes in each droplet using different fluorescent probes for each primer/probe set, to maximize the detection of tumor cells, given the heterogeneity of gene expression in CTCs. It is also possible to multiplex PCR primers and probes for multiple genes targeting different cancer types in each droplet, thus enabling the broad yet specific detection of multiple tumor types in a single assay.
  • Standard PCR cycling is performed on the entire emulsion sample using qPCR cycling conditions.
  • the reaction is carried to 45 cycles to ensure that the vast majority of individual droplet-PCR volumes are brought to endpoint. This is important because, although the reaction is performed with Taqman-type qPCR reagents and cycled under qPCR conditions, the fluorescent intensity of the sample will not be measured during the PCR cycling, but rather in the next step.
  • each individual droplet will be either a fully fluorescent droplet or will contain virtually no fluorescence at all. This enables the simple enumeration of all positive (fluorescent) and negative (non-fluorescent) droplets.
  • each positive droplet should represent a single originating RNA transcript. This interpretation depends on the number of individual droplets far exceeding the number of target cDNA molecules. In the new process, at one extreme we consider the possibility of a single CTC being isolated and lysed, releasing some number of RNA transcripts which are then reverse-transcribed 1:1 into cDNA, partitioned, PCR-amplified, and enumerated.
  • each cell contains approximately 80-120 copies of KLK3 mRNA.
  • the Biorad QX200 ddPCR System generates 20,000 droplets, which ensures that for small numbers of isolated CTCs and moderately-expressed target genes there will never be more than one target cDNA molecule per droplet.
  • approximate numbers of originating transcript can be estimated using Poisson statistics.
  • Candidate tumor-specific transcripts used to detect CTCs in blood are first selected by analyzing publicly available gene expression data sets derived from breast, prostate, lung, pancreas, and liver cancers and melanoma, as well as our lab-generated single cell RNASeq data from CTCs isolated from breast, prostate and pancreatic cancer patients and mouse models of these cancers. Transcripts whose expression is restricted to tumors and absent or undetectable in blood components are chosen for further downstream analysis. Demonstrating and validating total absence of expression (with the highest level of sensitivity, i.e., Digital PCR assays) in normal blood cells is important. In general, we found that only ⁇ 10% of candidate genes predicted based on computational models or RNA Seq data are truly negative in human blood samples.
  • candidate tumor-specific mRNA transcripts for the detection of CTCs were initially identified through the analysis of gene expression data sets (microarray and RNA-Seq) derived previously for human breast, prostate, lung, pancreas, hepatocellular, and melanoma cancers.
  • TCGA Cancer Genome Atlas
  • CCLE Cancer Cell Line Encyclopedia
  • RNA-seq gene expression data from CTCs isolated from human patients with breast, prostate, and pancreatic cancers were analyzed (GEO accession numbers GSE51827, GSE60407, and GSE67980) (Aceto et al., Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis, Cell, 158:1110-1122 (2014); Ting et al., Single-Cell RNA Sequencing Identifies Extracellular Matrix Gene Expression by Pancreatic Circulating Tumor Cells, Cell Rep, 8:1905-1918 (2014); and Miyamoto et al., RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance, Science 349:1351-1356 (2015).
  • Tumor specific transcripts identified through these databases were then compared to human leukocyte RNA-Seq gene expression data (GEO accession numbers GSE30811, GSE24759, GSE51808, GSE48060, GSE54514, and GSE67980). Transcripts that displayed significant differential expression, with high expression in tumors and low or undetectable expression in leukocytes, were then selected for further downstream analysis. Moreover, a literature search was performed to select additional candidate tumor-specific transcripts. Between 50 and 100 candidate genes were selected for each type of human cancer.
  • PCR primers For each candidate gene within each specific cancer type, two to four sets of PCR primers were designed to span regions across the target transcript. Primers are synthesized by IDT (Integrated DNA Technologies), probes are labeled with FAM or HEX, ZEN, and IABkFQ to create a probe targeting the middle of the amplicon.
  • Unique features of our PCR primer design methodology necessary for the successful application of digital PCR-based mRNA transcript detection in human CTCs include the following: 1) the specific targeting of the 3′ end of each mRNA transcript, given the proclivity of cellular mRNA transcripts to degrade from the 5′-end, particularly in unfixed, fragile cells such as CTCs; 2) the design of primers to generate amplicons that span introns in order to exclude the unintentional amplification of contaminating genomic DNA, for example from excess contaminating leukocytes in the enriched CTC mixture; and 3) the design of primers to inclusively amplify multiple splice variants of a given gene, given the uncertainty in some cases regarding the clinical relevance of specific splice variants.
  • the specificity of the primers was first tested by qRT-PCR using cDNA derived from cancer cell lines (representing breast, prostate, lung, pancreas, and liver cancers and melanoma). For each type of human cancer, 2 to 5 established cancer cell lines were cultured and used for initial testing to evaluate PCR primer performance and assess for expression of the target transcript in the specified cancer. To provide an initial test of specificity, the same primers were used to evaluate expression of the target transcript in leukocytes from healthy individuals who do not have a diagnosis of cancer. Leukocytes from a minimum of five different healthy individuals were tested in this phase of testing (mixture of male and female individuals—this was dependent on the type of cancer; i.e. candidate prostate cancer and breast cancer genes required the use of male or female healthy donors only, respectively).
  • RNA extraction and first-strand cDNA synthesis was performed for cancer cell lines and isolated leukocytes using standard methods. The specificity of expression of each gene (using 2 to 4 distinct sets of primers for each gene) was tested using qRT-PCR (cell line cDNA as positive controls, leukocyte cDNA from healthy donors as negative controls, and water as an additional negative control). Transcripts present in cancer cell lines, but absent in leukocytes based on qRT-PCR testing were then selected for further validation by droplet digital PCR. The selection criteria to pass this stage of testing were highly stringent, and required qRT-PCR signal to be present in at least one cancer cell line and absent in all healthy donor leukocyte samples tested.
  • CPT Cell Preparation Tubes with Sodium Heparin
  • Target transcripts and specific primer pairs that passed the qRT-PCR stage of testing were further validated using droplet digital PCR.
  • the CTC-iChip see, e.g., Ozkumur et al., “Inertial focusing for tumor antigen-dependent and -independent sorting of rare circulating tumor cells,” Sci Transl Med, 5, 179ra147 (2013) was used to process whole blood samples donated by healthy individuals.
  • the CTC-iChip performs negative depletion of red blood cells, platelets, and leukocytes from whole blood, and generates a sample product that is enriched for cells in the blood that do not express leukocyte markers, including CTCs (which should not be present in healthy individuals).
  • RNA stabilization solution RNAlater®, Life Technologies
  • Droplet digital PCR Biorad, CA
  • Samples assessed by droplet digital PCR during this phase of testing included cDNA from cancer cell lines, leukocyte cDNA from healthy donors processed through the CTC-iChip (at least four healthy individuals per primer pair being tested), and water as a negative control.
  • Criteria for passing droplet digital PCR testing were stringent, and included: 1) the presence of transcript signal in cancer cell lines (at least one cell line with >10 positive droplets); 2) excellent signal-to-noise ratio represented by separation of signal between positive and negative (empty) droplets; 3) minimal or absent droplet signal in healthy donors ( ⁇ 3 droplets per healthy donor); and 4) absent droplet signal in water (0 positive droplets).
  • CTCs are heterogeneous even within individual patients in their expression patterns
  • specificity detection of multiple gene signals confers added confidence that this represents a true cancer cell signature
  • the gene list provided below in Table 1 includes transcripts that are unique to specific types of cancer (e.g., highly specific markers of prostate or breast or liver cancers), as well as genes that are shared by several cancer types, e.g., all epithelial cancer types (and thus may serve as pan-cancer markers), and genes that are induced in certain conditions (e.g., active androgen signaling in prostate cancer or active estrogen signaling in breast cancer).
  • each type of cancer was assigned a specific panel of genes that is designed for optimal sensitivity, specificity, and clinically actionable information for the given cancer type.
  • primers described in Table 2 are designed to pre-amplify some of the genes listed in Table 1, while maintaining their high specificity. If STA is a method of choice, these nested primers become additional components of each cancer panel.
  • Table 1 provides a list of names of genes (with (Genbank ID) and Sequence Identification numbers (SEQ ID NO)), along with cancer types for which they are selective (Br: breast, Lu: lung, Li: liver, Pr: prostate, Panc: pancreatic, Mel: melanoma).
  • optimized primer sets are listed for each gene (primers 1 and 2), along with the composition of the fluorescent primer probes (e.g., 6-FAMTM (blue fluorescent label) or HEXTM (green fluorescent label) for tagged probes, and ZEN-31ABkFQ quencher) for optimal visualization of the digital PCR product.
  • 6-FAMTM blue fluorescent label
  • HEXTM green fluorescent label
  • PRAME is also named MAPE (Melanoma Antigen Preferentially Expressed In Tumors), OIP4 (Opa-Interacting Protein OIP4), and CT130 (Cancer/Testis Antigen 130).
  • Table 2 lists nested primers designed to specifically pre-amplify the regions targeted by primers listed in Table 1.
  • RNA derived from CTCs To improve the detection of tumor-specific mRNA from minimal amounts of RNA derived from CTCs, we established a multiplex assay capable of testing many different gene transcripts from a minute amount of CTC-Chip product. This combines the higher sensitivity/specificity of using multiple independent genes, with the fact that the amount of input template is limited (and hence should not be diluted into multiple reactions).
  • Our assay includes 4 genes per reaction, with each gene being resolved uniquely in 2-dimensional space by selecting different ratios of fluorescent conjugated primers. Thus, in a single reaction, we can independently measure 4 gene transcripts without having to dilute the template. For different cancers, we have gone as far as up to 4 different reactions (i.e., up to 20 different gene transcripts), and with application of nested RT-PCR digital assays, there is no limit to the number of reactions that can be performed.
  • This multiplex strategy achieves the ideal balance between analyzing multiple transcripts (and hence ensuring against heterogeneous variation in cancer cell expression patterns), but not diluting the input material by performing multiple independent PCR reactions.
  • assays ranging from 2-4 multiplex reactions (each multiplex reaction testing for 4-genes).
  • each individual gene amplification reaction has a unique combination of FAM/HEX signal that reflects the composition of the gene-specific primers, and hence identifies the gene-specific PCR product.
  • FAM blue
  • HEX green
  • 2-dimensional space we can illustrate the signal position of 4 different gene amplification products produced from a single multiplex reaction.
  • this method separates the targets into individual clusters by modifying the binary signal amplitude of positive droplets, which are displayed quantitatively. As predicted, this method allows both cumulative scoring of total signal for multiple genes (e.g., 16 markers in a total of 4 reactions), while also retaining the ability to quantify the signal from each individual gene target.
  • breast cancer mammography is considered effective, but even then a large number of breast biopsies are performed to diagnose each true malignancy.
  • lung cancer the recently recommended low dose CT scanning of individuals with a heavy cigarette smoking history is also likely to detect hundreds of innocent radiographic abnormalities for each true malignancy.
  • the d-CTC assays described herein can be used for both initial screening and as a confirmation of earlier screenings at a later time.
  • the assays can be used as a second-line test to validate a highly sensitive, but nonspecific screening test (e.g., PSA in prostate cancer).
  • a highly sensitive, but nonspecific screening test e.g., PSA in prostate cancer
  • routine periodic blood screening using the assays described herein may become the norm to monitor a patient's status or condition over time.
  • the new d-CTC readouts are also highly relevant to the serial monitoring of patients, e.g., seemingly healthy patients with a family history and/or genetic markers of a specific type of cancer, or patients with advanced or metastatic cancer. Imaging of CTCs is expensive and relatively insensitive, in that intact cells that stain appropriately for all required markers produce a single signal.
  • the new d-CTC assay also allows analysis of specific signaling pathways that are unique to the tumor cells in the blood. For instance, a subset of prostate lineage-specific genes are driven by androgen signaling (such as PSA), while another subset are repressed by androgen signaling (such as PSMA). By analyzing these genes together, we can ascertain the status of androgen signaling within CTCs. Similarly, in breast cancer, expression of estrogen-responsive genes (such as progesterone receptor) provides a measure of the status of the estrogen-responsive pathway within CTCs.
  • the new methods described herein are illustrated in prostate cancer, where the anti-androgenic agent abiratorone (e.g., ZYTIGA®) is effective in suppressing cancer progression, particularly in tumors that are still dependent on the androgen pathway.
  • abiratorone e.g., ZYTIGA®
  • transcripts that are specifically expressed in prostate tumor cells were selected several transcripts that are specifically expressed in prostate tumor cells, but are absent in contaminating leukocytes. These were the prostate lineage specific markers KLK3 (kallikrein-related peptidase; aka Prostate Specific Antigen, or PSA), FOLH1 (Folate Hydrolase; aka Prostate Specific Membrane Antigen, or PSMA) and AMACR (alpha-methylacyl-CoA racemase), as well as EpCAM (Epithelial Cell Adhesion Molecule). PCR conditions were optimized using intron-spanning primers and ZEN double-quenched FAM-labelled probes from Integrated DNA Technologies (Coralville, Iowa) following standard qPCR protocols.
  • FIG. 1A shows cDNA dilutions prepared from total RNA of LNCaP prostate cancer cells, mixed with leukocytes and analyzed by droplet PCR using two different prostate primer sets. The results represent several purities and show good response of positive droplet number across this range.
  • FIG. 1B shows manually isolated LNCaP cells spiked into HD blood samples, run through the iChip, and subjected to droplet RT-PCR (KLK3 primer set). The results show excellent sensitivity down to low numbers of target cells.
  • FIG. 1C shows the analysis of blood samples from healthy controls, patients with localized (resectable) prostate cancer and metastatic prostate cancer, processed through the CTC-iChip, subjected to RT-PCR and droplet analysis using three prostate-specific and one epithelial-specific biomarkers (KLK3, AMACR, FOLH1, EpCAM). The results are shown for the total number of droplets/ml for all four markers combined.
  • This example provides a general digital CTC assay protocol that can be used for the methods described herein. Different aspects of this general protocol were used in some of the Examples described herein. For example, Approach 1 of Step 3 of the protocol described below (relating to RNA purification to cDNA synthesis), was used to generate data for FIGS. 15A to 15C . Approach 2 in Step 3 was used to generate data for FIGS. 19A to 24B .
  • Patient blood is run through I-Chip, version 1.3M or 1.4.5 T.
  • Sample is collected in a 15 mL conical tube on ice.
  • Sample is spun down at 4 C. Supernatant is decanted and SUPERaseTM In (DTT independent RNAse inhibitor)+RNALater® Stabilization Solution (prevents RNA degradation by inhibiting RNAses) is added to the pellet. Sample is flash frozen and placed at -80 until further processing. Samples are stable at ⁇ 80.
  • SUPERaseTM In DTT independent RNAse inhibitor
  • RNALater® Stabilization Solution prevents RNA degradation by inhibiting RNAses
  • cDNA (synthesized from Approach 1 or 2) can be processed in two different ways:
  • cDNA from step 4a or 4b
  • Biorad SupermixTM for probes, primer or primers (for gene of interest; up to 4 different primers (FAM and HEX) can be multiplexed) were added in a total volume of 22 ⁇ l.
  • a master-mix which includes the ddPCR supermix and primers, was aliquoted into wells followed by addition of patient cDNA to each well and mixed well.
  • FIG. 2 demonstrates the use of a single gene transcript (KLK3, also known as PSA, for prostate cancer) as a probe (in the assay, we use from 8-24 gene transcripts, thereby further increasing sensitivity).
  • KLK3 also known as PSA, for prostate cancer
  • the blood is then processed through the CTC-Chip and subjected to digital readout as described above. No signal is observed in blood that has not been spiked with a single cancer cell. Introduction of 2 cells/10 ml of blood generates clear signal (65 positive droplets). In this case, the 10 CTC product was divided into 4 and run in quadruplicate, so the 64 droplets actually represent the digital signal derived from 1 ⁇ 4 of a tumor cell.
  • This assay is both highly sensitive and reproducible. As shown in FIG. 3 , the digital signal in these spiked cell experiments shows high reproducibility (2 independent replicates shown here), and the same amount of signal is seen when cells are spiked into buffer (rather than blood) and directly analyzed (without CTC-Chip processing). Thus, there is virtually no loss of signal when a tumor cell is diluted into billions of normal blood cells and then “re-isolated” using the CTC-Chip prior to digital readout.
  • the new assays include multiple genes, e.g., 2, 3, 4, 6, 8, 10, or more genes per reaction, with each gene being resolved uniquely in 2-dimensional space by selecting different ratios of fluorescent conjugated primers.
  • a single reaction one can independently measure 2, 3, 4, or more gene transcripts without having to dilute the template.
  • one can run and analyze multiple different reactions e.g., up to 20 different gene transcripts in four runs), and with application of nested RT-PCR digital assays, there is no limit to the number of reactions that can be performed.
  • each individual gene amplification reaction has a unique combination of FAM/HEX signal that reflects the composition of the gene-specific primers, and hence identifies the gene-specific PCR product.
  • FAM and HEX fluorescent probes
  • this method separates the targets into individual clusters by modifying the binary signal amplitude of positive droplets, which are displayed quantitatively. As predicted, this method allows both cumulative scoring of total signal for multiple genes (e.g., 16 markers in a total of 4 reactions), while also retaining the ability to quantify the signal from each individual gene target.
  • Probe 3 Mixture of FAM and HEX—sum up to 100%
  • Probe 4 Mixture of FAM and HEX—sum up to 100%
  • FIG. 4 shows the results of processing a normal control blood sample from a healthy donor (HD) through the CTC-Chip and subjected to d-CTC assay for 4 different gene transcripts, all of which are negative (i.e., blank droplets).
  • FIG. 5 is a representation of data from spiked cell experiments, prostate cancer cell lines introduced into blood and processed through the CTC-Chip, followed by digital assay, showed positive signal (fluorescent droplets) for each of the 4 lineage transcripts. These appeared at separate locations within the 2-Dimensional plot, based on differential fluorescence of two probes (color coded in picture). As the sample is overloaded with tumor cells, some droplets contained signal from more than one gene transcript (multiple genes per droplet are shown in gray).
  • the strategy of representing four different genes within each reaction was applicable to multiple different cancers, with specific lineage markers substituted for each tumor type. For instance, in prostate cancer, we predicted (theoretical model) a multiplex reaction with four quadrants (one gene per quadrant) for each of 2 reactions (total of 8 gene markers).
  • the spiked cell experiment prostate cancer cells introduced into control blood and processed through the CTC-iChip precisely recapitulated the predicted results.
  • FIGS. 6A-6B and FIGS. 7A-7B show that when assembled together, our analytic program integrated all positive signals within quadrants, just as predicted from modeling, and allowing us to develop methods to score the specific gene signals. Multi-dimensional space analysis of signal allowed for automated analysis and scoring with high level accuracy.
  • FIGS. 6A and 6B show the theoretical model and actual results, respectively, for a prostate cancer cell line for Reaction 1
  • FIGS. 7A and 7B show the theoretical model and actual results, respectively, for the same prostate cancer cell line for Reaction 2.
  • FIGS. 8A-8B (breast and lung cancer theoretical and actual results, Reaction 1), 9 A- 9 B (breast and lung cancer theoretical and actual results, Reaction 2), 10 A- 10 B (same, Reaction 3), 11 A- 11 B (same Reaction 4), 12 A- 12 B (same, Reaction 5), and 13 A- 13 B (same, Reaction 6) illustrate the results when the same approach was use with breast cancer and lung cancer.
  • Target-Specific Pre-Amplification to Improve Detection of Tumor-Specific mRNA
  • a nested PCR strategy was optimized for each of the gene-specific amplifications.
  • cDNA derived from the CTCs was first amplified with gene-specific primers which are situated a few base pairs external to the gene-specific primers used for d-CTC assay.
  • gene-specific primers which are situated a few base pairs external to the gene-specific primers used for d-CTC assay.
  • two to three primer sets were tested, and the primer set that is compatible with the gene-specific d-CTC assay primer and tests negative in HD blood was chosen for analysis of patient samples.
  • the target specific amplification protocol was first tested in cell lines derived from the different cancers.
  • the primer combinations that are specific for tumor cells (and absent in leukocytes) were then tested with a mixture of cancer cell lines mixed into blood and enriched through the CTC-iChip.
  • HD blood processed through the CTC-iChip was used as control.
  • Key to this strategy is the design of the nested PCR conditions to enhance the signal from minute amounts of CTC-derived cDNAs, without increasing the minimal baseline signal from normal blood cells. This selectivity was achieved by careful optimizing of PCR primer sequences and assay conditions, as well as balancing the cycle number for the external and internal PCRs.
  • FIG. 14 shows the droplet PCR signal for 7 markers (PIP, PRAME, RND3, PKP3, FAT1, S100A2, and AGR2) from 1 ng of non-amplified cell-line cDNA and from 1 ⁇ l of pre-amplified product after 10, 14, and 18 cycles of pre-amplification. Additional cycles of pre-amplification result in signal increase.
  • markers PIP, PRAME, RND3, PKP3, FAT1, S100A2, and AGR2
  • the assays described herein have been validated using actual patients samples from clinical studies. These include patients with metastatic cancer (lung, breast, prostate and melanoma), as well as patients with localized cancer (prostate). The assays are conducted as described in Examples 2 through 5.
  • FIGS. 15A , B, and C show a summary of clinical assays from patients with metastatic cancers of the lung (6 patients; FIG. 15A ), breast (6 patients; FIG. 15B ) and prostate (10 patients; FIG. 15C ) showed that virtually all patients have positive signal, whereas healthy controls have none. In this assay, all positive scores were added (cumulative score). However, as described below, the scores can also be broken down by individual genes, as shown in FIG. 16 .
  • FIG. 16 illustrates the cumulative analysis of data from multiple probes, and shows a positive signal in 10/11 metastatic prostate cancer patients (91% on a per patient basis) versus 0/12 (0%) of healthy controls. On a per sample basis, 24 of 28 samples had a positive signal, indicating an 86% detection rate.
  • some individual markers were also fairly effective, e.g., AGR2 (9/10 detection for metastatic cancer, and 0/3 for localize cancer), TMPRSS2 (5/10 and 1/3), KLK2 (6/10 and 0/3), STEAP2 (1/10 and 1/3), FAT1 (2/10 and 1/3), and FOLH1 (3/10 and 1/3)
  • a patient with metastatic prostate cancer had multiple positive markers
  • a patient with localized prostate cancer has a smaller number of positive scores within fewer markers
  • a healthy control is negative for all markers.
  • FIG. 17 shows clinical data from three representative patient samples.
  • a blood sample from a patient with metastatic prostate cancer showed multiple signals (all probes are positive to various degrees).
  • a blood sample from a patient with localized (curable) prostate cancer showed weaker (but clearly detectable) signal.
  • probes 1 (TMPRSS2), 5 (KLK3), 6 (HOXB13), 7 (AGR2) had the strongest signal in the metastatic cancer patient
  • probes 2 (FAT1) and 4 were most positive in the localized cancer patient. This result clearly illustrates the heterogeneity in signal among cancer cells in the blood and the importance of dissecting the differential signals within the assay.
  • Blood from a HD control processed identically to the cancer patient samples) had a complete absence of signal.
  • our d-CTC assay In addition to providing a digital (quantitative) measure of CTCs present within a blood sample, our d-CTC assay also allowed analysis of specific signaling pathways that are unique to the tumor cells in the blood. For instance, a subset of prostate lineage-specific genes were driven by androgen signaling (such as PSA), while another subset was repressed by androgen signaling (such as PSMA). By analyzing these genes together, we can ascertain the status of androgen signaling within CTCs. Defining the total number of CTC signal in the blood, simultaneously with information about the effectiveness of the therapeutic agent in targeting and shutting off the critical pathway is important for therapeutic monitoring.
  • FIG. 18 provides the results of a clinical study of a patient with metastatic prostate cancer.
  • the subset of signals from “androgen receptor-induced genes (AR-On)” is shown in green at the top of the bars in this bar graph, while the subset of signals from “androgen-repressed genes (AR-Off) is shown in red at the bottom of each bar.
  • abiratorone e.g., ZYTIGA® (abiraterone acetate
  • the AR-On signal is greatly reduced, indicating effective suppression of the androgen pathway within cancer cells in the blood.
  • the androgen pathway appears to be reactivated in cancer cells (increasing green signal), indicative of drug resistance. Serum PSA measurements taken at these time points are consistent with failure of drug treatment.
  • non-specific whole transcriptome amplification can be used to increase the detection rate of CTC-specific transcripts.
  • This method relies on the use of random primers that amplify not only the targets of interest but all messages found in the product.
  • the SMARTerTM Ultra Low RNA kit protocol (Clontech) was used as described below:
  • SSM Second Strand Synthesis and Amplification
  • FIGS. 19A and 19B show three different replicates of SMARTer-preamplified cDNA (18 cycles) from a liver cancer cell line (HEPG2) analyzed with 12 probes from the liver cancer panel.
  • HEPG2 liver cancer cell line
  • FIG. 19A while the amplification efficiency for each target region is different, it is consistent among the three replicates (WTA1, WTA2, WTA3), demonstrating the reproducibility of this approach.
  • FIG. 19B these methods using 18 cycles of SMARTer pre-amplification provide an increase in signal of approximately four orders of magnitude (10 8 vs 10 4 ), providing a great boost in detection.
  • HCC patients are defined as biopsy-confirmed non-resected hepatocellular carcinoma
  • CLD patients are patients with liver disease of varying etiologies (alcohol-mediated, HBV, HCV) who have negative ultrasound/MRI.
  • HD are healthy donors external to the lab who donate 10-20 mL of blood.
  • FIGS. 20A to 20C show the total droplet numbers in 21 hepatocellular carcinoma (HCC) patients ( FIG. 20A ), 13 chronic liver disease (CLD) patients ( FIG. 20B ) and 15 healthy donors (HDs)( FIG. 20C ).
  • CLD hepatocellular carcinoma
  • the sensitivity and specificity of each assay are dependent on the threshold values chosen to define “diseased” vs. “non-diseased,” but using 20 ug/L, the AFP gene marker has a sensitivity between 50-80% and a specificity between 80-90%. In a study using 20 ng/ml as the cut-off point, the sensitivity rose to 78.9%, although the specificity declined to 78.1% (Taketa, Alpha-fetoprotein, J. Med. Technol., 1989; 33:1380). On the other hand, the overall detection rate of the present assay was 76% when taking into account the clinical history of the patients and correcting for the ones that received curative resection or liver transplant with 100% specificity.
  • the top 5 markers (AHSG, ALB, APOH, FGB and FGG) by themselves have 70% sensitivity
  • the top 3 markers alone result in 67% sensitivity.
  • ALB alone detected 56% of the cases.
  • RNA samples from 8 metastatic lung patients and 8 healthy donors were processed through the CTC-chip as previously described. Samples were spun down, treated with RNAlaterTM and stored at ⁇ 80 C. RNA was purified and cDNA was synthesized as described. STA was performed on each sample using 6 ⁇ l cDNA and the nested primers corresponding to the probes listed in the figure. 1 ⁇ l of STA product was loaded per each droplet PCR reaction.
  • Droplet numbers were normalized to blood volume. As shown in FIGS. 21A and 21B , the multiplexed lung gene marker panel was able to detect 100% (8/8) metastatic lung cancer patient samples above the background of the 8 healthy donors. The sensitivity of each marker of the lung panel was also determined and the results show that SFRP had a detection rate of 8/8, FAT1 Probe 2 had a detection rate of 7/8,TMPRSS4 had a detection rate of 6/8, FOXF1 and ARG2, Probe 2 had a detection rate of 5/8, FAT1 had a detection rate of 4/8, FAT2 and AGR2 had a detection rate of 3/8, and FAT2, Probe 2 had a detection rate of 2/8.
  • RNA and cDNA from each sample were prepared as previously described. 6 ⁇ l cDNA from each sample was STA amplified using nested primers corresponding to the probes listed in FIG. 22 (FAT2, SCGB2A1, PGR, PRAME, TFAP2C, S100A2, FAT1, AGR2, PKP3, RND3, and PIP). Droplet numbers were normalized to blood volumes and the highest healthy donor value for each marker was subtracted from the patient sample values.
  • FIG. 22 shows the above-background signal for each patient. These methods detected 7/9 (78%) of metastatic samples and 2/5 (40%) of localized samples. The sensitivity of each marker alone varied from 1/14 to 6/14, with the two most relevant markers being AGR2 (6/14) and FAT1 (5/14), and the next four most relevant markers being RND3, PKP3, PRAME, and SCGB2A1 (3/14 each).
  • RNA and cDNA from each sample were prepared as previously described. 6 ⁇ l of non-amplified cDNA were loaded into each droplet PCR reaction. The samples were analyzed with probes against the v7 isoform of the androgen receptor (ARv7, sequence in Table 1). Droplet number was normalized to blood volume.
  • ARv7 was detected in 5/10 patients (50%) at above background (HD) levels, demonstrating that the assay is successful at detecting ARv7 from liquid biopsy.
  • One of the patients had a triple negative breast cancer, suggesting utility of ARv7 as a marker even in the triple negative breast cancer (TNBC) context (e.g., patients who do not express genes for any of the three most common breast cancer markers, the estrogen receptor (ER), HER2/neu, and the progesterone receptor (PR) marker).
  • TNBC triple negative breast cancer
  • RNA and cDNA from each sample were prepared as previously described. 12 ⁇ l cDNA from each sample was amplified by specific target amplification (10 cycles) using nested primers corresponding to the probes listed along the bottom of the graph in FIG. 24A (individual markers PMEL, MLANA, MAGEA6, PRAME, TFAP2C, and SOX10)). Droplet numbers were normalized to blood volumes.
  • 24B shows a dot plot distribution of droplet signals detected in melanoma patients as compared to healthy donors.
  • the detection sensitivity was 81% for all patient draw points (a patient draw is scored positive if any 1 of 6 markers shows droplet signals above the highest background signal in HD for that particular marker).
  • PMEL and MLANA showed the highest detection rate.

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