Molecular testing is increasingly recognized for guiding patient management and development of new targeted therapies. Meanwhile, there is an increased demand to perform testing on smaller volumes of tissue. Recent literature reports the use of ultrasensitive techniques to detect DNA mutations and translocations from stained cytology smears [1].

by Dr Laleh Hakima, Dr Maja H. Oktay and Prof. Sumanta Goswami

Introduction
Mutational analyses are crucial for guiding treatment decisions. This is particularly important for targeted therapies with tyrosine kinase inhibitors (TKI) for lung non-small cell carcinoma (NSCC) and for surgical management of thyroid nodules with indeterminate cytological diagnoses. Both lung and thyroid lesions are frequently diagnosed using fine need aspiration (FNA). FNA is a preferred method of obtaining diagnostic samples compared to surgical excisions or core biopsies. The FNA procedure is minimally invasive, rapid, cost-effective and has reduced procedure-related complications.

Molecular diagnostic tests of cytological samples are often performed using cytology cell block preparations. However, insufficient cellularity of cell blocks is a frequent limitation to these tests. In addition, errors introduced by formalin fixation may also interfere with accurate detection of mutations. Recent studies have reported successful testing using cytology smears stained with Diff-Quik®, which are air dried and fixed in alcohol. This approach has several advantages compared to testing on paraffin-embedded tissue (PET). Alcohol is a great preservative for DNA and RNA. The sample quality is superior to PET because FNA samples are typically enriched for cancer cells and cytology smears yield intact whole nuclei rather than the nuclear fragments that are obtained by cutting PET [2]. Lastly, testing performed directly on microdissected tumour cells ensures that material is sufficient and representative of the tumour and results in a faster turnaround time. We used cytological direct smears stained with Diff-Quik® and Papanicolaou (Pap) to detect commonly encountered mutations in non-small cell lung carcinoma and thyroid carcinoma [1].

Lung- and thyroid-carcinoma mutations and fusion gene testing targets
Approximately 64% of all lung adenocarcinomas harbour somatic driver mutations. According to The Lung Cancer Mutation Consortium, the frequency of EGFR and KRAS mutations are 23% and 25%, respectively. The incidence of EML4–ALK translocation, mainly detected in non-smoker patients with wild-type EGFR and KRAS genes, is approximately 6% [2, 3]. Other less frequent mutations include: BRAF ~3%, PIK3CA ~3%, MET amplifications ~2%, ERBB2 (HER2/NEU) ~1%, MAP2K1 ~0.4%, and NRAS ~0.2% [2]. Additionally, new driver mutations have recently been identified in lung cancer patients [3]. The most common mutations in thyroid carcinoma involve BRAF, KRAS, and RET/PTC genes. The mutations in BRAF and KRAS are point mutations, whereas RET (PTC) mutations are gene rearrangements that result in fusion of the tyrosine kinase domain of the RET gene to various unrelated genes [4–7].

The EGFR mutation status of the cancer is associated with its responsiveness or resistance to EGFR TKI therapy. The EGFR gene is located on chromosome 7p11.2, spans about 200kb, and contains 28 exons. The gene encodes a protein of 464 amino acids. The EGFR protein is composed of an N-terminal extracellular ligand-binding domain, a transmembrane lipophilic segment, and a C-terminal intracellular region containing a tyrosine kinase domain. The EGFR tyrosine kinase modulates cell proliferation and survival. Activation of EGFR initiates signalling cascades involving several downstream pathways, including Ras GTPases, which induce crucial cellular responses, such as proliferation, differentiation, motility, and survival. EGFR mutations associated with objective responses to single-agent TKI therapy in lung adenocarcinomas are preferentially observed in females of East Asian ethnicity who are never smokers and have adenocarcinoma with lepidic growth pattern (formerly bronchioloalveolar carcinoma). In adenocarcinomas, the majority of mutations have been identified in exons 18–21 of the EGFR gene. These mutations can be roughly classified into three major categories: in-frame deletions in exon 19, insertion mutations in exon 20, and missense mutations in exons 18–21 [8].

The fusion of the echinoderm microtubule-associated protein-like 4 (EML4) gene to the anaplastic lymphoma kinase (ALK) gene, EML4–ALK, is the most common fusion and results from the joining of exons 1–13 of EML4 to exons 20–29 of ALK. At least seven EML4–ALK variants (V1–V7) have been identified in lung adenocarcinomas. All seven variants are formed through the fusion of the intracellular tyrosine kinase domain of ALK with a variably truncated EML4 gene promoter. Activated ALK is involved in the inhibition of apoptosis and the promotion of cellular proliferation through activation of downstream PI3K/AKT1- and MAPK1-signalling pathways. The key downstream effectors on the ALK pathway include the Ras-activated protein, mitogen-activated protein kinase 1 [MAPK1; also known as extracellular signal regulated kinase (ERK)], phosphatidylinositol 3-kinase (PI3K), and signal transducer and activator of transcription 3 (STAT3) signalling pathways. Ras/MAPKK1/MAPK1 pathways are critical for cell proliferation, whereas the PI3K/AKT1 and STAT3 pathways are important for cell survival. The histology of these tumours is typically characterized by mucin production and either a solid growth pattern containing signet ring cells in Western patients or an acinar growth pattern in Asian patients. Compared with patients with wild-type ALK and EGFR, patients with the EML4–ALK fusion gene tend to be younger, of Asian ethnicity, diagnosed at an advanced clinical stage at presentation, male dominant, and more likely to be never smokers, but with a comparable response rate to chemotherapy and overall survival. The EML4–ALK fusion gene is typically detected by fluorescence in-situ hybridization (FISH). It has been reported that although ALK-fusion-positive lung cancers are resistant to the EGFR TKIs, gefitinib, and erlotinib, they are sensitive to small molecule TKIs against ALK [8].

The BRAF^V600E point mutation involves nucleotide 1799 and results in a valine-to-glutamate substitution at residue 600 (V600E). B-Raf is a serine/theronine protein kinase involved in MAPK/ ERK signalling pathway and in regulation of cell proliferation and differentiation. It is found in approximately 40–45% of papillary thyroid carcinomas. Not all variants are equally affected; 60% of classic papillary, 80% of tall cell variant, and 10% of follicular variant harbour this mutation. Its detection is clinically significant because if represents a prognostic marker for thyroid papillary carcinoma, it is associated with extrathyroidal extension, advanced tumour stage at presentation, and lymph node or distant metastases. BRAF^V600E point mutation is also an independent predicator of treatment failure and tumour recurrence, even with patients with low-stage disease [4].

Case selection and molecular analysis techniques
Thirty-one cases of lung adenocarcinomas and 26 thyroid carcinomas (17 classic papillary, 7 follicular variant, and 2 follicular carcinomas) were selected from the archives. Molecular analysis was performed on PET and from either Pap or Diff-Quik^® stained smears for each case. The following mutations in lung adenocarcinomas were tested: EGFR point mutations in exons 20 and 21, in-frame deletions in exon 19; KRAS point mutations in codons 12, 13, and 61; and EML4–ALK translocation. Thyroid carcinomas were tested for the BRAF^V600E point mutation.

Smears were reviewed by two cytopathologists and areas containing at least 50 cancer cells without necrosis or inflammation were marked for analysis [9, 10]. Tumour cells from marked areas were microdissected using RNA/DNA co-purification solution (Zymo Technologies) [5, 6]. QClamp xenonucleic acid (XNA) technology was used to detect mutations in EGFR, KRAS and BRAF genes. The qClamp, a wild-type sequence-specific XNA probe, has a melting point higher than 72 °C and remains attached to the wild-type DNA during the PCR extension stage. If there is a mutation, the probe dissociates from the template, which allows amplification. The technology was optimized using wild-type DNA (Promega, Madison, WI) resulting in a DNA shift of more than seven cycle thresholds (CTs) with the XNA clamp. In samples with mutations, the shift was less than 5 C_Ts (Fig. 1).

Quantitative RT-PCR (RT-qPCR) was used to detect EML–ALK translocations. PCR primers were used to amplify both the 3ʹ and 5ʹ ends of the ALK transcript (Aanera Biotech). In the presence of the translocation, the transcription of the EML4–ALK fusion gene (under the control of a stronger EML promoter) resulted in higher than expected 3ʹ ends than 5ʹ ends which lead to lower CT values for the 3ʹ transcript. A difference of more than 10 C_Ts between 3ʹ and 5ʹ ends was considered positive (Fig. 1).

The sensitivity of our assay was determined using two lung cancer cell lines, H1975 and H2228 (ATCC), harbouring the L858R mutation in the EGFR gene and the EML4–ALK translocation, respectively. The mutation detection rate was 1%.

Results
Approximately 80.6% of lung cases had some form of molecular alteration detected. There was 100% concordance between PET and cytology smears. All cases tested positive by our laboratory were also positive by the reference laboratory. However, seven cases that tested negative for EGFR mutation by the reference laboratory were found to be positive in our lab. In other words, clamp qPCR methodology detected approximately 20% more EGFR mutations than the reference laboratory. Likewise, three cases that were negative for KRAS by the reference laboratory, tested positive in our laboratory. In addition, there were six cases (19%) that had more than one molecular alteration in our cohort. Five cases had two mutations (three had EGFR exon 19 and KRAS codon 12 mutations, two had KRAS codon 12 and EML4–ALK mutations) and one case had three mutations (EGFR exon 19 and 21 and KRAS codon 12 mutation). Although EGFR and KRAS mutations were previously thought to be mutually exclusive, our results confirm recent reports of simultaneous mutations detected in study samples [7]. Given the known heterogeneity of cancers this finding may be expected, but might not have been detected previously because most analyses are performed using standard-sensitivity techniques.

Of 26 thyroid cases, 18 (81%) were positive for BRAF^V600E mutation on both PET and cytology smears. Concordant results were obtained from both cytology smears and PET from cases tested by us and the reference laboratory. Both follicular carcinoma cases tested negative on cytology smears and PET for BRAF^V600E. The percentage of patients with a BRAF mutation detected by us was higher than expected from the literature (81% versus 50%) reflecting our ultrasensitive approach to mutation detection [10].

Conclusion
Our results indicate that qClamp technology and the RT-qPCR approach can be used successfully to detect most common targetable molecular alterations from cytology smears of lung and thyroid carcinomas. We report successful DNA and RNA isolation from both Diff-Quik^® and Pap stained smears. This methodology represents an ultrasensitive and accurate approach with a 1% mutation detection rate and a decreased turnaround time of 1 to 2 days. Such an ultrasensitive method for molecular testing is essential as smaller amounts of diagnostic material become available and targeted approaches will be aimed at both molecular alterations present in the majority of cells, and at those present in a minority of cells that potentially may represent a subpopulation susceptible to recurrence [11].

Abbreviations
Table 1 shows the gene symbols and the names and symbols of the proteins encoded by those genes.

References
1. Oktay MH, Adler E, Hakima L, Grunblatt E, Pieri E, Seymour A, Khader S, Cajigas A, Suhrland M, Goswami S. The application of molecular diagnostics to stained cytology smears. J Mol Diagn. 2016; 18(3): 407–415.
2. Tsao MS, Sakurada A, Cutz JC, Zhu CQ, Kamel-Reid S, Squire J, Lorimer I, Zhang T, Liu N, Daneshmand M, Marrano P, da Cunha Santos G, Lagarde A, Richardson F, Seymour L, Whitehead M, Ding K, Pater J, Shepherd FA. Erlotinib in lung cancer – molecular and clinical predictors of outcome. N Engl J Med. 2005; 353: 133–144.
3. Campbell JD, Alexandrov A, Kim J, Wala J, Berger AH, Pedamallu CS, Shukla SA, Guo G, Brooks AN, Murray BA, Imielinski M, Hu X, Ling S, Akbani R, Rosenberg M, Cibulskis C, Ramachandran A, Collisson EA, Kwiatkowski. DJ, Lawrence MS, Weinstein JN, Verhaak RG, Wu CJ, Hammerman PS, Cherniack AD, Getz G, Cancer Genome Atlas Research Network, Artyomov MN, Schreiber R, Govindan R, Meyerson M. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet. 2016; 48(6): 607–616.
4. Nikiforov YE. Molecular diagnostics of thyroid tumors. Arch Pathol Lab Med. 2011; 135(5): 569–577.
5. Gupta N, Dasyam AK, Carty SE, Nikiforova MN, Ohori NP, Armstrong M, Yip L, LeBeau SO, McCoy KL, Coyne C, Stang MT, Johnson J, Ferris RL, Seethala R, Nikiforov YE, Hodak SP. RAS mutations in thyroid FNA specimens are highly predictive of predominantly low-risk follicular-pattern cancers. J Clin Endocrinol Metab. 2013; 98(5): 914–922.
6. Nikiforov YE, Ohori NP, Hodak SP, Carty SE, LeBeau SO, Ferris RL, Yip L, Seethala RR, Tublin ME, Stang MT, Coyne C, Johnson JT, Stewart AF, Nikiforova MN. Impact of mutational testing on the diagnosis and management of patients with cytologically indeterminate thyroid nodules: a prospective analysis of 1056 FNA samples. J Clin Endocrinol Metab. 2011; 96(11): 3390–3397.
7. Nikiforov YE, Yip L, Nikiforova MN. New strategies in diagnosing cancer in thyroid nodules: impact of molecular markers. Clin Cancer Res. 2013; 19(9):2283–2288.
8. Cheng L, Alexander RE, Maclennan GT, Cummings OW, Montironi R, Lopez-Beltran A, Cramer HM, Davidson DD, Zhang S. Molecular pathology of lung cancer: key to personalized medicine. Mod Pathol. 2012; 25: 347–369.
9. Marchetti A, Milella M, Felicioni L, Cappuzzo F, Irtelli L, Del Grammastro M, Sciarrotta M, Malatesta S, Nuzzo C, Finocchiaro G, Perrucci B, Carlone D, Gelibter AJ, Ceribelli A, Mezzetti A, Iacobelli S, Cognetti F, Buttitta F. Clinical implications of KRAS mutations in lung cancer patients treated with tyrosine kinase inhibitors: an important role for mutations in minor clones. Neoplasia. 2009; 11: 1084–1092.
10. Russo M, Malandrino P, Nicolosi ML, Manusia M, Marturano I, Trovato MA, Pellegriti G, Frasca F, Vigneri R. The BRAF (V600E) mutation influences the short and medium-term outcomes of classic papillary thyroid cancer, but is not an independent predictor of unfavorable outcome. Thyroid 2014; 24: 1267–1274.
11. Pon JR, Marra MA. Driver and passenger mutations in cancer. Annu Rev Pathol. 2015; 10: 25–50.

The authors
Laleh Hakima¹ DO; Maja H. Oktay¹ MD, PhD; Sumanta Goswami*² PhD
¹Department of Cytopathology, Montefiore Medical Center Bronx, NY, USA
²Department of Biology, Yeshiva University, New York, NY, USA

*Corresponding author
E-mail: Goswami@yu.edu

The current prognostic staging of colorectal cancer (CRC) by the tumour, node, metastasis method, alongside the minimal core data set, provides good prognostic information for patient populations but is less accurate for the individual. Reporting of additional histopathological features can improve individualized prognostic staging, but manual microscopic surveillance often results in observer variability and there is a lack of consensus on standardized quantification methods. Automated image analysis can standardize the quantification of prognostic features, can personalize CRC prognosis and augment clinical staging.

by Dr Peter Caie

Introduction
Colorectal cancer (CRC) incidence is extremely high in the developed world, where it is the third most common cancer in men and women in both the UK and USA. There will be an estimated 134 500 new CRC cases diagnosed, and just under 50 000 deaths will have been caused by the disease, in the USA alone. Although the incidence rate has decreased only slightly in the last decade, the change in mortality has dropped significantly between 2000 and 2010, with ~50% of patients across all disease stages surviving for at least 5 years post-diagnosis. Reasons for the decrease in mortality include life style change (47% of CRC cases in the UK could be prevented from healthier lifestyle choice) early detection of disease (e.g. home testing such as fecal occult blood kits), targeted therapy as the result of ‘omics’ research, novel prognostic factors coupled with more accurate pathological and clinical staging of disease and advances in surgical technique. These factors culminate in a more effective treatment of patients at an early stage and at a personalized level. Survival rates in early stage cancer, where the tumour is localized, are extremely good with ~90% of patients experiencing 5-year disease-free survival. Upon spread to localized lymph nodes survival decreases to 50–70% and if distant metastasis has occurred survival is only 12% [1, 2].

Current prognostic staging of CRC
Although multiple CRC subtypes exist, with both molecular and histopathological variances, 90% of CRCs are adenocarcinomas and prognosis is determined through the international tumour, node, metastasis (TNM) staging system alongside the minimal core data set. The TNM staging is based on gross observation and analysis of histopathological tissue sections under the microscope which revolves around the depth of local invasion (T), presence of cancer within the lymph nodes (N) and if the cancer has metastasized (M). TNM staging is excellent at returning prognostic information on a population of patients; however, it is less specialized at predicting prognosis at the level of the individual [3]. A patient’s prognosis is worse the higher the stage they are classified within, however, the TNM system does not differentiate between good and poor outcome of patients within the same stage [4]. There are defined adjuvant treatment guidelines associated with the various stages of CRC [5]. Stage 0 and I cancers will not routinely receive adjuvant chemotherapy and surgical resection is considered curative.  Adjuvant therapy is recommended for stage III and IV patients, however, there remains ambiguity about whether to treat all, a subset or no stage II patients with adjuvant chemotherapy [6]. Around 30% of stage II CRC patients will succumb to their disease after surgical resection and, therefore, an accurate and more personalized identification and stratification of high-risk stage II patients, some of whom have comparable or worse outcomes than stage III patients [7], is therefore imperative to increase disease free survival rates.  In the UK, pathologists collect a minimal core data set for each patient which helps to identify high-risk stage II CRC cases [8]. Although some parameters within the data set are disregarded in clinical decision making for the management of stage II CRC patients, some features, if present, may invoke the decision to treat the patient: high grade/poor differentiation, pT4 local spread and extramural lymphovascular invasion [9]. However, there is little evidence to date to show the advantage of adjuvant therapy for stage II patients with additional high-risk factors. Furthermore, there are promising histopathological features listed in the literature that have been significantly correlated with poor prognosis but which rarely feature in final clinical reports. There is, for example, growing evidence that immune cell infiltrate and perineural invasion are strongly correlated with poor patient outcome, whereas lymphatic vessel invasion (LVI) and the invasive growth pattern, including tumour budding, are two of the most promising histopathological features that have been significantly associated with lymph node metastasis and disease-specific survival.

Manual reporting yields observer variability
Although histopathological features such as nuclear grade have long been established in the core data sets for CRC prognosis and features such as immune infiltrate, invasive pattern, LVI, lymphatic vessel density (LVD) and the tumour-to-stroma ratio are associated with poor prognosis in the literature, they have also been associated with observer variability in their reporting. Observer variability is an inevitable occurrence when reporting histopathological features by eye under microscopic surveillance; however, it is increased by certain features being obscure under H&E stained tissue with associated retraction artefact, difficult to accurately quantify or if they are rare events. This is particularly true when the calculation of areas in manually determined ‘hot-spots’ is required, such as for LVD and tumour-to-stroma ratio calculations which are both very prone to observer bias and variability. Furthermore, a general consensus on standardized quantification methods is lacking for many of these candidate histopathological features and for this reason they, apart from grading, have not translated into the minimal core data sets of CRC pathological reporting. Although nuclear grading has been reported as a minimal core data set for many years, there have been studies which have also found it to be non-significant [10]. Therefore, there is now a consensus for nuclear grading in CRC to move toward a two-tiered scoring system of ‘well differentiated’ and ‘poorly differentiated’ which eliminates the ‘moderately differentiated’ class and attempts to increase standardization.

Automated image analysis can standardize the quantification of prognostic features
Digital pathology and associated image analysis technology is becoming increasingly sophisticated. It is now possible to create image analysis algorithms that can automatically segment and quantify histopathological features within digital tissue sections with high accuracy. There are multiple advantages to applying image analysis to histopathology reporting that overcome the associated observer variability of manual scoring. Automated image analysis uses standardized algorithms and objectively reports on the features it is programmed to quantify. It does so in a robust manner across all patient samples being tested. Applying image analysis allows a higher degree of accuracy when reporting on the number, area or ratio of specific features across a whole tissue section and so negates the need of manually assigned hot-spots. Furthermore continuous data is captured when image analysis is applied, which allows more accurate clinical cut-offs to be used resulting in a more personalized assessment of a patient’s condition than the more traditional categorical reporting of, e.g. 1+, 2+, 3+ for immunohistochemistry, or ‘well’ or ‘poorly’ differentiated cases. Finally, rare and obscure events that may be missed by the eye are able to be reported with higher confidence when the computer assesses the entire issue section. Jeremy Jass in the late 1980s reported a novel grading system for CRC that included the reporting of the immune infiltrate and the pattern of invasive margin [11]; however, his promising results were not translated into the clinic due to poor reproducibility. Recently, two groups have used image analysis to quantify the immune infiltrate in the form of the immunoscore [12] and the tumour’s infiltrative pattern in the form of tumour budding [13] (Fig. 1A) in a manner that allows standardization, and have shown both features to be prognostically significant. Image analysis has further been used to quantify, amongst others, additional set histopathological features such as for nuclear grading [14], vasculature hot-spots [15] and LVI and LVD (Fig. 1B) [13].

Automated image analysis personalizes CRC prognosis
CRC is a specifically heterogeneous disease and the tumour microenvironment is also a heterogeneous and heterotypic ecology. Therefore, quantifying a single histopathological aspect of a patient’s tumour may not be sufficient for an accurate prognosis across a large population. A recent study by our group used multiplexed immunofluorescence to quantify a suite of histopathological features co-registered across a single CRC tissue section for each patient. Furthermore, the algorithm exported data captured across each nucleus (Fig. 2) in each sample to create a large and complex personalized multi-parametric data set for each patient in the stage II CRC study [16]. A single standardized image analysis algorithm was run across a training set of patients which quantified continuous data from the invasive front of stage II CRC on the number, shape and extent of: tumour buds, poorly differentiated clusters, LVI, LVD, tumour-to-stroma ratio, tumour gland morphology as well as multiple measurements across each nucleus to increase the accuracy of nuclear grading. The resultant ‘big-data’ was distilled through machine learning to identify the optimal parameter set to stratify patients into a high or low risk of disease-specific death. The result was the identification of a novel feature that was independently significant and where the addition of any other measured feature into the model added no further significance to patient stratification. This feature was the mean area of poorly differentiated clusters (area PDC) across the invasive front. The data from the training cohort was validated across a larger independent cohort and again the novel feature held more significance for patient risk stratification in stage II CRC than any of the other more established histopathological feature measured in the study. Furthermore, mathematical modelling was employed to identify if any of the parameters from the clinical pathology report added value to the prediction of disease specific death. By performing this analysis it was found that pT stage and differentiation added further value and were incorporated into a Novel Prognostic Index alongside the area PDC. This novel index outperformed the clinical gold standard of pT staging by almost twofold (Fig. 3).

Big-data and personalized pathology augments clinical staging
The idea behind big-data pathology is to include as much data as possible about each single patient and so to move towards a more personalized prognosis. The acquisition of quantitative data through image analysis and molecular pathology lends itself very well to big-data pathology, where the vast data sets can be mined through sophisticated machine learning algorithms to identify the optimal parameters to answer the clinical question. However, clinical staging and reporting has stood the test of time and it is imperative to include data such as this in any integrative model. As it becomes easier and cheaper to acquire large, reproducible and standardized data sets, modern pathology will become more personalized and patient outcome will improve due to tailored treatment regimens directed at individual patients.

References
1. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin. 2011; 61(4): 212–236.
2. Shah R, Jones E, Vidart V, Kuppen PJ, Conti JA, Francis NK. Biomarkers for early detection of colorectal cancer and polyps: systematic review. Cancer Epidemiol Biomarkers Prev. 2014; 23(9):1712–1728.
3. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet 2014; 383(9927):1490–1502.
4. Lea D, Haland S, Hagland HR, Soreide K. Accuracy of TNM staging in colorectal cancer: a review of current culprits, the modern role of morphology and stepping-stones for improvements in the molecular era. Scand J Gastroenterol. 2014; 49(10):1153–1163.
5. Poston GJ, Tait D, O’Connell S, Bennett A, Berendse S. Diagnosis and management of colorectal cancer: summary of NICE guidance. BMJ 2011; 343:d6751.
6. Dotan E, Cohen SJ. Challenges in the management of stage II colon cancer. Semin Oncol. 2011; 38(4):511–520.
7. Urquhart R, Bu J, Grunfeld E, Dewar R, MacIntyre M, Porter GA. Examining stage IIB survival in a population-based cohort of patients with colorectal cancer. Cancer 2012; 118(23):5973–5981.
8. Loughrey MB, Quirke P, Shephard NA. Standards and datasets for reporting cancers. Dataset for colorectal cancer histopathology reports July 2014. The Royal College of Pathologists 2014. The cancer datasets are a combination of  textual  guidance,  educational  information  and  reporting  proformas to enable consistent grading and staging. (https://www.google.co.uk/?gws_rd=ssl#q=Standards+and+datasets+for+reporting+cancers+Dataset+for+colorectal+cancer+histopathology+reports+July+2014)
9. Morris EJ, Maughan NJ, Forman D, Quirke P. Who to treat with adjuvant therapy in Dukes B/stage II colorectal cancer? The need for high quality pathology. Gut 2007; 56(10):1419–1425.
10. Ratto C, Sofo L, Ippoliti M, Merico M, Doglietto GB, Crucitti F. Prognostic factors in colorectal cancer. Literature review for clinical application. Dis Colon Rectum 1998; 41(8):1033–1049.
11. Jass JR, Love SB, Northover JM. A new prognostic classification of rectal cancer. Lancet 1987; 1(8545):1303–1306.
12. Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, Lugli A, Zlobec I, Hartmann A, et al. Towards the introduction of the Immunoscore in the classification of malignant tumors. J Pathol. 2014; 232(2):199–209.
13. Caie PD, Turnbull AK, Farrington SM, Oniscu A, Harrison DJ. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer. J Transl Med. 2014; 12:156.
14. Rathore S, Hussain M, Aksam Iftikhar M, Jalil A. Novel structural descriptors for automated colon cancer detection and grading. Comput Methods Programs Biomed. 2015; 121(2):92–108.
15. Kather JN, Marx A, Reyes-Aldasoro CC, Schad LR, Zollner FG, Weis CA. Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget 2015; 6(22):19163–19176.
16. Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [Epub ahead of print].

Acknowledgment
^{This article is based on the author’s recently published paper: Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 2016; doi: 10.18632/oncotarget.10053 [16].}

The author
Peter Caie PhD
Quantitative and Digital Pathology, School of Medicine, University of St Andrews, North Haugh, St Andrews, UK

*Corresponding author
E-mail: Pdc5@st-andrews.ac.uk

Children presenting with diabetic ketoacidosis (DKA) require prompt assessment and treatment initiation to prevent serious complications. The use of point-of-care (POC) analysers to assess blood ketones is beginning to replace the traditional analysis of urine ketones, but some questions remain as to their optimal utilization.

by Dr A.M. Ferguson, Dr J. Michael, Prof. S. DeLurgio and Dr M. Clements

Introduction
Diabetic ketoacidosis (DKA) is an acute complication of uncontrolled diabetes mellitus resulting from insulin deficiency. It is biochemically defined as hyperglycemia (blood glucose >200 mg/dL) with metabolic acidosis (venous pH <7.3 or bicarbonate <15 mmol/L), ketonemia, and ketonuria [1]. The clinical picture of the patient can include fatigue, polydipsia, polyuria, dehydration, abdominal pain, vomiting and altered mental status (Box 1). DKA can occur in known diabetics and can be the presenting symptom prior to diagnosis. Children who are on insulin pump therapy, who have unstable family situations, or have limited access to healthcare are at an increased risk of DKA [1], and DKA is the most common cause of diabetes-related mortality in children. Assessing urine ketones has been part of the standard practice when assessing if a patient has DKA, but this has multiple issues. There are three types of ketones: acetoacetate, acetone, and β-hydroxybutyrate (BHB). BHB is the predominant ketone produced during DKA and can be present at up to 10 times the amount of acetoacetate. The urine dipsticks that are commonly used to assess ketonuria utilize a nitroprusside reagent that reacts with acetoacetate and acetone but not at all with BHB. This is problematic because the major ketone produced in DKA is not detected, which can lead to false negative urine ketone testing. Additionally, as ketosis resolves, BHB is converted to acetoacetate, increasing urine ketones during the recovery phase, potentially leading the clinician to believe that the ketosis is worsening instead of resolving. An added obstacle is the difficulty of getting a urine specimen from a young child, especially one in nappies. Measuring serum ketones, specifically BHB, is a solution to both of these issues. Clinical measurement of serum ketones
As the methodology for measuring serum BHB became more automated, the test moved from being used only on a research basis to being available for clinical use. Initial studies were done to see how serum BHB functioned for the diagnosis of DKA. A large retrospective study looking at simultaneous measurements of BHB and bicarbonate found that BHB levels of ≥3 and ≥3.8 mmol/L in children and adults, respectively, could be used to diagnose DKA and provides a more specific assessment of DKA than bicarbonate alone [2].

When assessing patients for DKA, it is critical to make the diagnosis as quickly as possible to initiate treatment and prevent the patient from decompensating further. The commercial availability of point-of-care (POC) meters to assess serum ketones allows the patient to be tested immediately on presentation at the bedside. There have been multiple studies performed in adults showing that use of POC BHB meters in the emergency room can aid in diagnosis and treatment of DKA. Arora et al. compared POC BHB and urine ketone dipstick results in 54 patients with DKA presenting to the emergency department [3]. They found that both methods were equally sensitive for detecting DKA at 98.1%, but that BHB with a cut-off of ≥1.5 mmol/L is more specific for DKA compared to urine dipsticks (78.6 vs 35.1%) and could cut down on unnecessary DKA work ups in hyperglycemic patients. Another study found that a BHB value of 3.5 mmol/L yielded 100% sensitivity and specificity for the diagnosis of DKA [4].

Use of POC testing in pediatrics
Fewer studies have been done in pediatric patients. One such study by Ham et al. determined that using a POC meter in the hospital setting could aid in monitoring the resolution of DKA in pediatric patients [5]. The BHB values from the POC meter correlated with BHB values from the laboratory for most of the meter’s measurement range. Use of the meter had both a strong positive predictive value (PPV, 0.85) as well as negative predictive value (NPV, 1.0) for indicating the presence or absence of DKA at a meter value of 1.5 mmol/L [5]. Noyes et al. used POC ketone testing to identify the endpoint of an integrated care pathway when treating DKA in children [6]. They compared their current treatment endpoint of pH >7.3 and no presence of urine ketones with an endpoint defined by pH >7.3 and two successive POC ketone measurements of <1 mmol/L. The study measured time of treatment in 35 patient episodes in children ranging in age from 1–14 years. The time to completion of treatment using POC ketone measurement was 17 hours, compared to 28 hours using measurement of urine ketones to end treatment [6] . They found that occasionally a value below 1 mmol/L would be followed by a value above 1 mmol/L, but this never occurred after two subsequent values under 1 mmol/L, leading them to recommend waiting for the two successive low values before ending treatment. In addition to allowing an earlier treatment endpoint, this approach enables less time to be spent in the ICU, with decreased cost associated with treatment. Using a POC ketone meter can also result in fewer tests being ordered overall. Rewers and colleagues asked whether monitoring serum BHB values at the bedside could result in a decrease in laboratory testing in pediatric patients [7]. Their results indicated that the real-time changes observed in POC serum BHB values correlated strongly with changes in pH, bicarbonate, and pCO2 and also had good correlation with the laboratory BHB method. While initial measurement of pH, bicarbonate and pCO2 is encouraged, following up the patient with POC BHB can replace serial laboratory measurements of those analytes and decrease the amount of laboratory testing [7]. Similarly, a separate study showed that use of a POC BHB meter at home decreased diabetes-related hospital visits and hospitalizations of pediatric diabetics when compared to urine ketone testing by allowing earlier identification of ketosis and initiation of treatment [8]. Most of the studies mentioned are close to 10 years old, but measuring serum BHB to diagnose DKA or monitor its resolution has not become standard practice. A recent review of the standard treatment guidelines for DKA in children and adolescents raises the question of whether blood ketones should be evaluated during management of DKA [9]. The authors recommend using serum BHB measurement, either from the laboratory or at the point of care, to both diagnose DKA and monitor treatment. Despite the inaccuracies of POC meters seen at high BHB values [5–7], use of a diagnostic cut-off of >3 mmol/L is well within the accurate range of the meters and can be used to confidently diagnose DKA and monitor the patient’s response to treatment.

Conclusions
Despite the increasing body of knowledge indicating that measurement of serum BHB can aid in both diagnosis and management of DKA, a study conducted in 2014 indicated that although 89% of pediatric emergency medicine and critical care providers responding to a survey stated that they had a DKA protocol at their institution, 67% perceived no clinical advantage in the use of serum ketone measurements [10]. This suggests that evaluation of serum ketone monitoring during DKA management from a quality improvement and research perspective may be necessary before clinical adoption is widespread. The next iteration of DKA management guidelines should address the potential utility of serum ketone monitoring.

References
1. Wolfsdorf J, Craig ME, et al. Diabetic ketoacidosis in children and adolescents with diabetes. Pediatr Diabetes 2009; 10(Suppl 12): 118–133.
2. Sheikh-Ali M, Karon BS, et al. Can serum beta-hydroxybutyrate be used to diagnose diabetic ketoacidosis? Diabetes Care 2008; 31(4): 643–647.
3. Arora S, Henderson SO, et al. Diagnostic accuracy of point-of-care testing for diabetic ketoacidosis at emergency-department triage: {beta}-hydroxybutyrate versus the urine dipstick. Diabetes Care 2011; 34(4): 852–854.
4. Charles RA, Bee YM, et al. Point-of-care blood ketone testing: screening for diabetic ketoacidosis at the emergency department. Singapore Med J. 2007; 48(11): 986–989.
5. Ham MR, Okada P, White PC. Bedside ketone determination in diabetic children with hyperglycemia and ketosis in the acute care setting. Pediatr Diabetes 2004; 5(1): 39–43.
6. Noyes KJ, Crofton P, et al. Hydroxybutyrate near-patient testing to evaluate a new end-point for intravenous insulin therapy in the treatment of diabetic ketoacidosis in children. Pediatr Diabetes 2007; 8(3): 150–156.
7. Rewers A, McFann K, Chase HP. Bedside monitoring of blood beta-hydroxybutyrate levels in the management of diabetic ketoacidosis in children. Diabetes Technology & Therapeutics 2006; 8(6): 671–676.
8. Laffel LM, Wentzell K, et al. Sick day management using blood 3-hydroxybutyrate (3-OHB) compared with urine ketone monitoring reduces hospital visits in young people with T1DM: a randomized clinical trial. Diabet Med. 2006; 23(3): 278–284.
9. Wolfsdorf JI. The International Society of Pediatric and Adolescent Diabetes guidelines for management of diabetic ketoacidosis: Do the guidelines need to be modified? Pediatr Diabetes 2014; 15(4): 277–286.
10. Clark MG, Dalabih A. Variability of DKA management among pediatric emergency room and critical care providers: a call for more evidence-based and cost-effective care? J Clin Res Pediatr Endocrinol. 2014; 6(3): 190–191.

The authors
Angela M. Ferguson*¹ PhD, DABCC, FACB; Jeffery Michael1 D.O., FAAP; Stephen DeLurgio² PhD; Mark Clements¹ MD, PhD, CPI
¹Children’s Mercy Hospital, Kansas City, MO, USA
²Bloch School, University of Missouri, Kansas City, MO, USA

*Corresponding author
E-mail: amferguson@cmh.edu