Mecanismo Plural de Seguimiento a la Agenda de Desarrollo

1.7.1 Tumour sampling bias

Given that therapeutic decision-making is frequently based on archival tumour material or, in cases where patients present with advanced disease, a single metastatic sample, tumour sampling bias may present a significant clinical challenge (Swanton, 2012). Indeed, if different regions of the same tumour are genetically distinct, one sample will not be representative of the tumour and, by

extension, if biomarkers are not clonal, sampling bias may confound clinical decision-making.

In support of difficulties associated with validating clinical biomarkers, potentially due to sampling bias, it has been documented that while over 150,000 biomarkers have been proposed in the literature, approximately only 100 of these are used in clinical practice (Poste, 2011). Moreover, evidence for multiple distinct prognostic biomarkers within single tumours can be found in KIRC, where seven out of ten tumours were found to harbour distinct gene expression signatures, one associated with good prognosis, and the other associated with poor prognosis (Gulati et al., 2014). Likewise, an analysis of 11 glioblastomas revealed that most patients harboured multiple different subtypes within the same tumour (Sottoriva et al., 2013).

Sampling bias may also lead to underestimates for the prevalence of particular mutations in cancers. Notably, in KIRC, an analysis in our lab found that single sample analysis suggests the prevalence of mutations in TP53 is only 10%, while multi-region analysis, with more complete sequencing of cancer cells, identified mutations in TP53 in approximately 40% of tumours (Gerlinger et al., 2014a).

1.7.2 Heterogeneity and outcome

1.7.2.1 Clinical relevance of chromosomal instability

It has long been established that chromosomal instability – resulting in cell-to-cell genetic heterogeneity – is associated with poor prognosis across a wide range of cancers (see Table 1). For instance, multiple studies in NSCLC using FISH and gene expression signatures to measure CIN have found an association between CIN status and overall survival, independent of conventional risk factors, including tumour stage (Carter et al., 2006, Mettu et al., 2010, Choi et al., 2009). In breast cancer, CIN has been linked to poor prognosis and also is enriched in more aggressive subgroups of breast cancer, such as ER-negative and triple-negative.

Furthermore, evidence has suggested that this form of genomic instability is associated with multi-drug resistance in colorectal cancer (Lee et al., 2011a).

The potential benefits of chromosomal instability, and intra-cellular genetic heterogeneity, must be balanced against its potentially deleterious consequences. An abnormal chromosome number in yeast and murine systems has been demonstrated to be deleterious to normal cells, reducing proliferation (Torres et al., 2007a, Williams et al., 2008). However, other studies have suggested chromosomal instability may redress imbalances in the stoichiometry of protein complexes which can result from an abnormal chromosome complement and CIN may effectively allow proliferation genes to be hardwired to the genome (Ozery- Flato et al., 2011). It has thus been proposed there is an optimal level of CIN in tumours, beyond which it becomes unfavourable (Swanton, 2012, Cahill et al., 1999). The notion of extreme CIN being deleterious for tumour development may be analogous to ‘mutational meltdown’ in bacteria, or error catastrophe in viruses (McGranahan et al., 2012).

Consistent with excessive levels of CIN having adverse consequences for tumour progression, excessive CIN, induced by inactivation of spindle assembly checkpoint components, leads to excessive aneuploidy and cell death in human cancer cells, and multipolar cell divisions generate non-viable cells that are highly aneuploid (Giam and Rancati, 2015). Similarly, mice with reduced levels of CENP- E – involved in spindle elongation – develop tumours with CIN. However, if CIN is increased through depletion of CENP-E in tumours that already had a pre-existing level of aneuploidy, the depletion can have tumour-suppressive abilities (Sotillo et al., 2007).

Table 1-2 Clinical significance of chromosomal instability

Cancer Type Method of measuring CIN

CIN associated with Reference

Lung cancer (NSCLC)

FISH (n=63) Poor prognosis (OS & DFS) (Choi et al., 2009)

FISH (n=47) Poor prognosis (OS) (Yoo et al., 2010)

FISH (n=50) Poor prognosis (OS) (Nakamura et al., 2003)

12-gene signature (n=647) Poor prognosis (OS) (Mettu et al., 2010) CIN70 signature (n=62) Poor clinical outcome (Carter et al., 2006)

Breast cancer SSI (n=890) Poor prognosis (OS) (Kronenwett et al.,

2004)

SNP (n=313) Poor prognosis (MFS) (Smid et al., 2011)

12-gene signature (n=469) Poor prognosis (DFS & RFS)

(Habermann et al., 2009)

CIN70 signature (n=1866) Poor clinical outcome (Carter et al., 2006)

FISH (n=31) Lymph-node metastasis and

ER negativity. (Takami et al., 2001)

Myelodysplastic syndrome

FISH (n=65) Poor prognosis (DFS) (Heilig et al., 2010)

Endocrine pancreatic tumours

CGH (n=62) Metastasis (Jonkers et al., 2005)

Colon cancer 12 gene genomic instability signature (n=92)

Recurrence of colon cancer (Mettu et al., 2010)

Flow cytometry/ image cytometry (n = 10 126)

Poor prognosis. (Walther et al., 2008) Ovarian cancer 12-gene genomic instability

signature (n=124)

Poor prognosis (RFS). (Mettu et al., 2010) Endometrial

cancer SNP (n=31) Poor prognosis (OS). (Murayama-Hosokawa et al., 2010)

Synovial sarcoma

CGH (n=22) Poor prognosis (OS) (Nakagawa et al., 2006)

Oral cancer (SCCs)

FISH (n=77) Poor prognosis (OS & DFS) (Sato et al., 2010)

FISH (n=20) (Loco) regional

tumour outgrowth

(Bergshoeff et al., 2008) Diffuse Large B-

cell Lymphoma Anaphase segregation errors(n=54) Poor prognosis (RFS) (Bakhoum et al., 2011) Cervical cancer CIN70 signature (n=79) Para-aortic nodal relapse (How et al., 2015) Abbreviations: NSCLC, non-small cell lung cancer; SCC, squamous cell carcinoma; FISH, fluorescence in situ hybridization; SSI, stem line scatter index; CGH, comparative genome hybridisation; SNP, single- nucleotide polymorphisms, OS, overall survival; DFS, disease-free survival; MFS, metastasis-free survival; RFS, relapse free survival

Evidence substantiating this hypothesis has been found when further dissecting the relationship between CIN and cancer outcome. Specifically, patients who harbour tumours with extreme CIN, defined as tumours in the upper quartile of CIN70 expression, exhibit significantly better prognosis, in terms of recurrence-free or distance metastasis-free survival, compared to patients harbouring tumours in the third CIN70 expression quartile (Birkbak et al., 2011). This association has been

observed in ER negative breast, ovarian, gastric, breast and non-small cell lung cancers, but not in ER positive breast cancers (Birkbak et al., 2011).

1.7.2.2 Clonal heterogeneity and outcome

One of the first studies to specifically evaluate the clinical relevance of clonal heterogeneity adapted diversity measures from ecology and evolution to explore whether these could predict progression to adenocarcinoma in the premalignant condition Barrett’s oesophagus (Maley et al., 2006). Using the Shannon diversity index to capture both the number and abundance of clones, it was found that using multiple different types of somatic alterations, clonal diversity was predictive of progression to cancer in a large cohort of patients (Maley et al., 2006). These results have since been confirmed in an independent cohort of tumours, encompassing 239 patients (Merlo et al., 2010).

More recently, in CLL, it has been found that the presence of subclonal drivers is associated with a shorter time to retreatment or death (Landau et al., 2013) while in head-and-neck cancer, a measurement capturing the clonal diversity – termed mutant allele tumour heterogeneity (MATH) – was found to correlate with poor prognosis (Mroz and Rocco, 2013). However, in MDS, the number of driver events was the key determinant of outcome, regardless of their clonal status; i.e. the presence of a driver was more critical than whether it was subclonal or clonal (Papaemmanuil et al., 2013).

Clonal heterogeneity may also impact upon the efficacy of therapeutic treatments, through the presence of subclones harbouring resistance mutations, which may be barely detectable at diagnosis. In NSCLC with activating mutations in EGFR presence of subclonal gatekeeper T790M resistance mutations are associated with shorter progression free survival (Maheswaran et al., 2008, Su et al., 2012). In a small cohort of high grade serous ovarian cancers (n=14) treated with platinum- based chemotherapy a copy number based clonal heterogeneity index was found to have predictive value for survival after chemotherapy treatment (Schwarz et al., 2015).

Evidence in colorectal cancer (Diaz et al., 2012), as well as melanoma (Van Allen et al., 2014, Shi et al., 2014) suggests that multiple resistance events can occur independently in the same tumour. For instance, following BRAF inhibitor therapy in BRAF V600 mutant melanoma, resistance mutations in both NRAS and MEK1 were identified in one tumour while another patient presented with a tumour harbouring two distinct NRAS mutations (Van Allen et al., 2014). It has also been demonstrated that resistance to BRAF inhibitors can occur through both MAPK pathway dependent and PI3K-AKT dependent mechanisms in the same tumour simultaneously (Shi et al., 2014). Likewise, in one patient with colorectal cancer, through longitudinal tracking of cell free tumour DNA, four distinct KRAS mutations, that increased in frequency during the acquisition of resistance to panitumumab therapy (targeting EGFR), were detected (Diaz et al., 2012).