APOBEC3B expression analysis

Total RNA was isolated from tumour regions of which fresh frozen material was available (L001, L002, L003, L004 and L011), using the AllPrep DNA/RNA kit from Qiagen according to the manufacturer’s instruction, and used to synthesize cDNA. The cDNA was then amplified using APOBEC3B Taqman Assay (Applied Biosystems) on a 7500 FAST Real Time PCR machine (Applied Biosystems). Taqman Assays for the housekeeping gene TBP. APOBEC3B expression was normalised towards TBP, and the fold-change in expression was determined against the expression in the adjacent normal lung.

2.4.6 MHC multimer generation and combinatorial encoding-flow cytometry analysis

MHC-multimers holding the predicted neo-antigen were produced in-house at Technical University of Denmark, in the laboratory of Sine R. Hadrup. HLA molecules matching the HLA-expression of L011 (HLA-A1101, A2402, and B3501)

and L012 (HLA-A1101, A2402, and B0702) were refolded with a UV-sensitive peptide, and exchanged to peptides of interest following UV exposure (Toebes et al., Bakker et al., Frosig et al., Chang et al.). Briefly, HLA complexes loaded with UV-sensitive peptide were subjected to 366-nm UV light (CAMAG) for one hour at 4°C in the presence of candidate neo-antigen peptide in a 384-well plate. Peptide- MHC multimers were generated using a total of 9 different fluorescent streptavidin (SA) conjugates: PE, APC, PE-Cy7, PE-CF594, Brilliant Violet (BV)421, BV510, BV605, BV650, Brilliant Ultraviolet (BUV)395 (BioLegend). MHC-multimers were generated with two different streptavidin-conjugates for each peptide-specificity to allow a combinatorial encoding of each antigen responsive T cells, enabling analyses for reactivity against up to 36 different peptides in parallel (Hadrup et al., Andersen et al.).

2.4.7 Identification of neo-antigen-reactive CD8+ T cells

MHC-multimer analysis was performed on in-vitro expanded CD8+ T lymphocytes isolated from lung tumour regions and adjacent normal lung tissue. 290 and 355 putative epitopes (with predicted HLA binding affinity <500nM, including multiple potential peptide variations from the same missense mutation) were synthesized and used to screen expanded L011 and L012 tumour infiltrating lymphocytes (TILs) respectively. For staining of expanded CD8+ T lymphocytes, samples were thawed, treated with DNAse for 10 min, washed and stained with MHC multimer panels for 15 min at 37°C. Subsequently, cells were stained with LIVE/DEAD® Fixable Near- IR Dead Cell Stain Kit for 633 or 635 nm excitation (Invitrogen, Life Technologies), CD8-PerCP (Invitrogen, Life Technologies) and FITC coupled antibodies to a panel of CD4, CD14, CD16, CD19 (all from BD Pharmingen) and CD40 (AbD Serotec) for an additional 20 min at 4°C. Data acquisition was performed on an LSR II flow cytometer (Becton Dickinson) with FACSDiva 6 software. Cut-off values for the definition of positive responses were ≥0.005% of total CD8+ cells and ≥10 event

Chapter 3. Evidence and extent of intra-tumour

heterogeneity

3.1 Introduction

Accumulating evidence suggests intra-tumour heterogeneity may be widespread across human cancers. The extent of this diversity within tumours has important clinical implications. For instance, in colorectal cancer, subclonal mutations in the oncogene RAS have been shown to precipitate resistance to cetuximab (Misale et al., 2012), while in NSCLC, subclonal EGFR T790M mutations are associated with resistance to EGFR-TKI (tyrosine kinase therapy) (Kobayashi et al., 2005). Further complicating the issue, the use of targeted therapy against a subclonal driver mutation, present in a subset of cancer cells within a tumour`, may lead to stimulation of wild-type subclones which lack the actionable alteration (Lohr et al., 2014).

Targeting clonally dominant somatic events, present in all tumour cells, or adopting combinatorial targeted therapy approaches, may therefore be necessary for optimal tumour control (Yap et al., 2012a). However, although the clonal status of driver mutations has received attention in certain cancers (Papaemmanuil et al., 2013, Bolli et al., 2014, Lohr et al., 2014, Shah et al., 2012, Nik-Zainal et al., 2012b, Landau et al., 2013, Gerlinger et al., 2012, Gerlinger et al., 2014a), a broad understanding of the heterogeneity of mutations in cancer genes, and deciphering their clonal and subclonal frequencies, is lacking.

More generally, an understanding of the patterns of cancer evolution may begin to shed light on whether rules dictating progression of cancers can be discerned. Such rules may inform clinical practice, and reveal novel avenues for drug discovery and clinical trial design. Relatedly, there is a need to understand the impact of therapy itself on tumour evolution and the extent of diversity within tumours.

In this chapter I explore the degree of intra-tumour heterogeneity across ten major cancer types. I make use of next-generation sequencing data from both multi- region sampling and single tumour sample data to quantify intra-tumour heterogeneity and tumour evolution. I investigate the extent to which both driver and passenger mutations are clonal or subclonal across cancers, focussing on heterogeneity at the single-nucleotide level. In addition, heterogeneity within tumours is used to time the acquisition of driver events in cancer evolution, revealing which events may be crucial for tumour transformation, whilst also shedding light on others which may play important roles in tumour progression and metastasis. Finally, I investigate the impact of neo-adjuvant platinum chemotherapy on tumour evolution and intra-tumour heterogeneity in oesophageal adenocarcinoma.

The data presented in this chapter of the thesis largely forms sections of three separate publications (McGranahan et al., 2015, de Bruin et al., 2014, Murugaesu et al., 2015), which can be found in Appendix 2.