Porphyrias are a group of disorders of the heme biosynthetic pathway which clinically manifest with acute neurovisceral attacks and cutaneous lesions. Diagnosis of porphyrias is based on the accurate and precise measurement of various porphyrins and precursor molecules in a range of samples. In addition, molecular diagnostic assays can provide definitive diagnosis.

by Dr Vivion E. F. Crowley, Nadia Brazil, and Sarah Savage

What are porphyrias?
Porphyrias are a group of rare disorders each of which results from a deficiency of an individual enzyme within the heme biosynthetic pathway (Fig. 1) [1–3]. With the exception of an acquired form of porphyria cutanea tarda (PCT), all porphyrias are inherited as monogenic autosomal dominant, autosomal recessive or X-linked genetic disorders, with varying degrees of penetrance and expressivity and this impacts on the prevalence and incidence of clinically manifest porphyrias [4].  The biochemical consequence of each porphyria is the overproduction within the heme biosynthetic pathway of specific porphyrin intermediates and/or the porphyrin precursor molecules delta-aminolevulinic acid (ALA) and porphobilinogen (PBG) [2–3]. This in turn has implications for the clinical manifestation of these disorders, their overall classification and their diagnosis (see Table 2).

Clinical presentation
Porphyrias may present clinically with either or both of two symptom patterns. The first is the acute neurovisceral attack, which is a potentially life threatening episode related to excessive hepatic generation of ALA and PBG, and which is a feature only in acute intermittent porphyria (AIP), variegate porphyria (VP), hereditary coproporphyria (HCP) and the very rare ALA dehydratase deficiency porphyria (ADP) [5–7]. These attacks are characterized principally by autonomic dysfunction, including non-specific but severe abdominal pain, constipation, diarrhoea, nausea, vomiting, tachycardia, hypertension or occasionally postural hypotension. In addition, other features may include a predominantly motor peripheral neuropathy which, if left undiagnosed, may extend to respiratory failure reminiscent of Guillain–Barré syndrome, as well as cerebral dysfunction, which can vary from subtle alterations in mental state, to posterior reversible encephalopathy syndrome (PRES). Hyponatremia, most likely due to SIADH [syndrome of inappropriate antidiuretic hormone (ADH) secretion] may also contribute to CNS-related morbidity. The complex neuropathic manifestations appear to be primarily related to axonal degeneration due to direct neurotoxicity by ALA, which structurally resembles the neurotransmitter gamma-aminobutyric acid (GABA) [3, 5–7].

The second clinical presentation paradigm is cutaneous photosensitivity caused by the interaction of ultraviolet light with photoactive porphyrins in the skin resulting in the production of reactive oxygen species (ROS) and an associated inflammatory response [3]. In PCT, VP and HCP the skin lesions typically occur post-pubertally and consist of skin fragility, vesicles, bullae, hyperpigmentation and hypertrichosis affecting sun exposed areas, most usually the face and dorsum of hands [1–3]. In erythropoietic protoporphyria (EPP) and X-linked protoporphyria (XLP), which may present in childhood, there is usually no blistering but instead erythema, edema and purpura feature in the more acute setting, with subsequent chronic skin thickening noted, whereas congenital erythropoietic porphyria (CEP) is characterized by severe cutaneous photosensitivity often occurring in early infancy with bullae and vesicles rupturing and being prone to secondary infection, with resultant scaring, bone resorption, deformation and mutilation of sun-exposed skin [1, 2, 8].

Classification
The classification of porphyrias (Table 1) has traditionally been determined either on the basis of clinical manifestations, i.e. acute or non-acute (cutaneous), or on the primary organ of porphyrin overproduction, i.e. hepatic or erythropoietic [1, 3, 8]. A combined classification has recently been proposed which takes account of both of these elements [2]. However, whichever classification is adopted there should be a realization that VP, and to a lesser extent HCP, can manifest with both acute and cutaneous features either simultaneously or separately.

Clinical and biochemical diagnosis
The clinical manifestations of porphyrias, particularly the acute hepatic porphyrias, are protean and consequently, patients with a clinically active porphyria could initially present to a relatively wide spectrum of clinical specialties including, gastroenterology, acute medicine, dermatology, neurology, endocrinology and hematology amongst others [2]. In general, cutaneous porphyrias should not pose a diagnostic difficulty for an experienced dermatologist used to investigating photosensitive skin disorders, but biochemical testing is still required to define the type of porphyria present. However, definitive diagnosis of an initial acute hepatic porphyria attack is critically dependent on biochemical testing, as symptoms are often non-specific in nature (Tables 1 & 2).

The diagnosis of an acute hepatic porphyria attack is founded on demonstrating an increase in urine PBG levels in direct temporal association with the characteristic acute symptom complex, the minimum level of increase being between 2- and 5-fold [9, 10]. The urine PBG may be measured either as a random sample, where it should be reported as urine PBG to creatinine ratio or as a 24-hour urine collection, where total PBG is reported.  The former has proven to be clinically efficacious and has the advantage of timeliness, reduced within-subject variation and convenience over the requirement for a 24 hour urine collection [9]. If the urine PBG is not elevated this effectively rules out an acute porphyria attack at the time of sampling, however, there are certain caveats to this. Thus it is important to note that if specific treatment with either heme preparations or carbohydrate loading has been instigated prior to the test these interventions could reduce the urine PBG level significantly, including normalization [3]. Furthermore, if the measurement of urine PBG is delayed or undertaken at a time removed from the actual acute clinical presentation e.g. by weeks or months, then the finding of a normal urine PBG at that later stage cannot effectively rule out acute porphyria [3]. In this authors experience another important caveat concerns patients with a previous confirmed diagnosis acute porphyria who present with symptoms suggestive of recurrent acute attack. In many instances these patients have a perpetually elevated urine PBG, even in between attacks, and therefore an elevated urine PBG cannot effectively guide diagnosis. In these situations a decision to treat as an acute attack has to be made on the basis of clinical findings.
 
Therefore, a clinically effective service for acute porphyria diagnosis requires that a timely, quality assured laboratory method for urine PBG should be available for analysis [11]. Although a qualitative method for urine PBG may suffice for the purposes of establishing a diagnosis this should be supported by the availability of a confirmatory quantitative method for urine PBG. The lack of availability of urine PBG assay is very often the basis for misdiagnosis or indeed delayed diagnosis of acute porphyria attacks [10].

In conjunction with PBG, urine ALA is often measured simultaneously and although also elevated it does not tend to reach the levels of PBG in acute porphyrias. The one exception is the extremely rare instance of autosomal recessive ADP due to defective ALA synthase 2 (ALAS2) activity, where markedly elevated urine ALA levels are reported while PBG may be normal or only slightly elevated [2, 3]. In addition, a similar pattern of urine ALA predominance relative to PBG (although not as elevated) may be observed in the context of lead poisoning, wherein patients may also present with abdominal pain and neuropathy [1, 3].

Once the diagnosis of acute porphyria has been made based on the urine PBG the next phase involves determining the type of porphyria present. This is very much dependent on the specific pattern of porphyrin overproduction observed in samples of urine, feces, plasma and erythrocytes. It is critically important that the laboratory analytical methods available extend beyond the sole measurement of total porphyrin levels [10–12]. In particular, it is essential that individual porphyrin analysis and isomer fractionation in both urine and feces is available to facilitate the identification of the porphyria-specific patterns of porphyrin overproduction [10–12]. In many instances non-porphyria disorders affecting the gastrointestinal and hepatobiliary systems or certain dietary factors may cause non-specific secondary elevations in porphyrins, e.g. coproporphyrinuria, which can be diagnostically misleading [3]. In such cases urine PBG levels will not be elevated and the pattern of porphyrins observed will not be indicative of any one of the specific porphyrias per se. Therefore, it is important to realize that a finding of elevated porphyrin levels does not automatically equate to a diagnosis of underlying porphyria. This further highlights the importance of developing specialist porphyria centres to ensure that the appropriate repertoire of quality assured testing and expert interpretation and support are available for diagnosis and management of porphyria patients [11, 13].

The diagnosis of cutaneous (non-acute) porphyrias is also very much based on the specific patterns of porphyrins observed in urine and feces. In addition, the pattern of free and zinc protoporphyrin in erythrocytes can be useful in the diagnosis of CEP, EPP and the related disorder, XLP. Moreover, the identification of the porphyria subtype, either acute or cutaneous, may also be enhanced by identifying characteristic plasma porphyrin fluorescence emission peaks, e.g. VP emission peak between 625 and 628 nm [1–3]. Finally, it is essential that all samples for porphyrin and precursor measurement are protected from light prior to analysis.

Role of genetic diagnosis
Given the heritable nature of porphyrias it is not surprising that molecular genetic analysis has also become an important diagnostic adjunct. There is an extensive allelic heterogeneity of pathogenic mutations among the implicated genes for each porphyria disorder, which means that most mutations are uniquely confined to one or at most a few kindreds. There are, however, a few exceptions to this trend, most notably in relation to founder mutations among the Swedish population and the Afrikaner population in South Africa.  The general approach in the application of genetic diagnostic strategies is firstly to characterize the causative mutation in a known affected individual (proband) using a mutation scanning approach [14]. Once a putative mutation has been identified its pathogenicity for a particular porphyria should be affirmed and then more extensive family cascade genetic screening can be organized based on the analysis of this kindred-specific mutation [14].

This approach has important implications in the diagnosis of porphyria susceptibility, particularly for the autosomal dominant acute hepatic porphyrias, where both penetrance and expressivity of the disorders is low [3, 4]. Thus the penetrance among AIP, VP and HCP is between 10 and 40%, implying that the majority of patients with an autosomal dominant acute hepatic porphyria will not manifest with an acute attack (or indeed cutaneous lesions in the case of VP and HCP) in their lifetime [3, 4]. Moreover, this lack of penetrance may also extend to the absence of subclinical biochemical abnormalities indicative of an underlying autosomal dominant acute porphyria, demonstrating the limited sensitivity of biochemical testing in identifying asymptomatic family members.

 Currently there is no clear-cut mechanism for discriminating between those who will manifest a clinical and/or biochemical phenotype and those who will not. While the role of environmental precipitating factors, e.g. porphyrinogenic medications, stress, prolonged fasting, menstruation [1–3], have long been recognized in triggering acute porphyria attacks, it is the presence of a pathogenic mutation which is still the single most important factor determining the overall susceptibility for an acute porphyria episode.  Therefore, all patients carrying a pathogenic mutation should be regarded as pre-symptomatic carriers, i.e. capable of developing an acute attack, and one of the key applications of genetic analysis in the area is in identifying pre-symptomatic carriers to allow for appropriate counselling and management advice to prevent attacks [3, 14].

In this author’s experience another useful role for molecular diagnostics in porphyrias is in relation to those patients with an historic diagnosis of acute hepatic porphyria in whom the biochemical abnormalities have subsequently normalized over years.  In such instances genetic analysis can provide a definitive diagnosis for the type of porphyria and will accommodate a more extensive family screening programme for potential pre-symptomatic carriers.

The current methods of genetic analysis vary but usually involve a confirmatory step using direct nucleotide sequencing of the putative pathogenic variants as the gold standard. However, the emergence of next generation sequencing platforms has further galvanized the diagnostic possibilities in this area. Overall, in autosomal dominant acute hepatic porphyrias, approximately 95% of mutations are identifiable [3, 14]. This sensitivity includes the application of additional methods such as ‘multiplex ligation-dependent probe amplification’ (MLPA) and gene dosage analysis for identifying complex mutations, such large gene deletions, which may not be detected using standard sequencing-based approaches [14].

In autosomal recessive porphyrias including ADP, CEP and EPP, the clinical penetrance approaches 100%. These disorders also display a level of genetic heterogeneity. In the case of EPP the presence of a relatively common low expression single nucleotide polymorphism (SNP) located in the ferrochetalase gene, FECH (IVS3-48C), appears to be essential for the clinical expression of the cutaneous phenotype in the vast majority of cases [15].

The application of molecular genetics has provided a means of establishing definitive porphyria susceptibility, however, similar to the situation for biochemical testing services any genetic diagnostic services in this area must be quality assured to a high standard and need to adopt appropriate mutation scanning assay validation protocols in accordance with international standards and best practice recommendations [11–14].

References
1. Puy H, Gouya L, Deybach JC. Porphyrias. Lancet 2010; 375(9718): 924–937.
2. Balwani M, Desnick RJ. The Porphyrias: advances in diagnosis and treatment. Blood 2012; 120: 4496–4504.
3. Badminton MN, Elder GH. The porphyrias: inherited disorders of haem synthesis. In: Marshall W, Lapsley M, Day A, Ayling R, editors. Clinical Biochemistry Metabolic and Clinical Aspects. Churchill Livingstone Elsevier 2014; pp. 533–549.
4. Elder G, Harper P, Badminton M, Sandberg S, Deybach JC. The incidence of inherited porphyrias in Europe. J Inherit Metab Dis. 2013; 36: 849–857.
5. Simon NG, Herkes GK. The neurologic manifestations of the acute porphyrias. J Clin NeuroSci. 2011; 18: 1147–1153.
6. Sonderup MW, Hift RJ. The neurological manifestations of the acute porphyrias. S Afr Med J. 2014; 104: 285–286.
7. Crimlisk HL. The little imitator-porphyria: a neuropsychiatric disorder. J Neurol Neurosurg Psychiatry. 1997; 62: 319–328.
8. Siegesmund M, van Tuyll van Serooskerker AM, Poblete-Gutierrez P, Frank J. The acute hepatic porphyrias: Current status and future challenges. Best Pract Res Gastroenterol. 2010; 24: 593–605.
9. Aarsand AK, Petersen PH, Sandberg S. Estimation and application of biological variation of urinary delta-aminolevulinic acid and porphobilinogen in healthy individuals and in patients with acute intermittent porphyria. Clin Chem. 2006; 52: 650–656.
10. Kauppinen R, von und zu Fraunberg M. Molecular and biochemical studies of acute intermittent porphyria in 196 patients and their families. Clin Chem. 2002; 48: 1891–1900.
11. Aarsand AK, Villanger JH, Støle E, Deybach JC, Marsden J, To-Figueras J, Badminton M, Elder GH, Sandberg S. European specialist porphyria laboratories: diagnostic strategies, analytical quality, clinical interpretation and reporting as assessed by an external quality assurance programme. Clin Chem. 2011; 57: 1514–1523.
12. Whatley S, Mason N, Woolf J, Newcombe R, Elder G, Badminton M. Diagnostic strategies for autosomal dominant acute porphyrias: Retrospective analysis of 467 unrelated patients referred for mutational analysis of HMBS, CPOX or PPOX gene. Clin Chem. 2009; 55: 1406–1414.
13. Tollånes MC, Aarsand AK, Villanger JH, Støle E, Deybach JC, Marsden J, To-Figueras J, Sandberg S; European Porphyria Network (EPNET). Establishing a network of specialist porphyria centres – effects on diagnostic activities and services. Orphanet J Rare Dis. 2012; 7: 93.
14. Whatley SD, Badminton MN. The role of genetic testing in the management of patients with inherited porphyria and their families. Ann Clin Biochem. 2013; 50: 204–216.
15. Gouya L, Puy H, Robreau AM, Bourgeois M, Lamoril J, Da Silva V, Grandchamp B, Deybach JC. The penetrance of dominant erythropoietic protoporphyria is modulated by expression of wildtype FECH. Nat Genet. 2002; 30: 27–28.

The authors
Vivion E. F. Crowley*¹ MB MSc FRCPath FFPath(RCPI) FRCPI, Nadia Brazil² BA (Mod) FAMLS, Sarah Savage³ BSc MSc
¹Consultant Chemical Pathologist, Head of Department, Biochemistry Department, St James’s Hospital, Dublin 8, Ireland
²Porphyrin Laboratory, Biochemistry Department, St James’s Hospital, Dublin 8, Ireland
³Molecular Diagnostic Laboratory, Biochemistry Department, St James’s Hospital, Dublin 8, Ireland

*Corresponding author
E-mail: vcrowley@stjames.ie

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.

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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