Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumours.

Oncogenic fusions represent compelling druggable targets in solid tumours highlighted by the recent site agnostic FDA approval of larotrectinib for NTRK rearrangements. However screening for fusions in routinely processed tissue samples is constrained due to degradation of nucleic acid as a result of formalin fixation., To investigate the clinical utility of semiconductor sequencing optimised for detection of actionable fusion transcripts in formalin fixed samples, we have undertaken an analysis of test trending data generated by a clinically validated next generation sequencing platform designed to capture 867 of the most clinically relevant druggable driver-partner oncogenic fusions. Here we show across a real-life cohort of 1112 patients with solid tumours that actionable fusions occur at high frequency (7.4%) with linkage to a wide range of targeted therapy protocols including seven fusion-drug matches with FDA/EMA approval and/or NCCN/ESMO recommendations and 80 clinical trials. The more prevalent actionable fusions identified were independent of tumour type in keeping with signalling via evolutionary conserved RAS/RAF/MEK/ERK, PI3K/AKT/MTOR, PLCy/PKC and JAK/STAT pathways. Taken together our data indicates that semiconductor sequencing for detection of actionable fusions can be integrated into routine diagnostic pathology workflows enabling the identification of personalised treatment options that have potential to improve clinical cancer management across many tumour types.

Introduction

Oncogenic fusion genes are an important class of driver mutation in solid tumours and haematological malignancies [1]. The rationale for targeting gene fusions was first highlighted by the significant clinical responses to imatinib in patients with BCR-ABL-positive chronic myeloid leukaemia [2]. This therapeutic approach has now been extended to solid tumours, for example the targeting of ALK, ROS1, NTRK and RET fusion genes in non-small cell lung cancer (NSCLC) where the clinical impact has been far reaching with markedly improved survival outcomes [3, 4].

For decades, the major pathway for development and approval of oncology drugs has centred on histopathological parameters, anatomical location and clinical data from all-comers clinical trials [5]. However, the ecosystem for drug development has now changed dramatically with the recent landmark FDA approval for larotrectinib targeting neurotrophic receptor tyrosine kinase (NTRK) oncogenic fusions and pembrolizumab for tumours exhibiting microsatellite instability (MSI) and/or mismatch repair (MMR) defects. These drugs have been approved based on efficacy linked to specific molecular aberrations and not on conventional clinicopathological parameters [6, 7]. This new functional tissue agnostic approach to targeted therapies and immunotherapies is being rapidly accelerated by the expansion of molecular basket clinical trials in which detection of an actionable genetic variant is used as the determinant for entry into a clinical trial [8, 9].

Although many oncogenic fusion genes have now been identified, few are screened as potential therapeutic targets in routine clinical practice. Testing is mostly restricted to the detection of ALK and ROS rearrangements in NSCLC [10]. This relates to the fact that fusion gene analysis is a particular challenge in the clinical context due to the complex combination and numbers of driver genes and partner genes involved in chromosomal rearrangements [1, 11–14]. Moreover, the nucleic acid templates extracted from routine formalin fixed paraffin wax embedded tissues (FFPE) biopsy samples are characterised by low DNA/RNA yields with poor integrity and quality which is difficult to sequence particularly in relation to RNA fusion transcripts. Indeed, the challenges of genomic profiling of FFPE clinical samples was recently highlighted in the summary report of the Genomic England 100,000 genomes project which utilized fluorescent based sequencing and concluded that analysis of FFPE samples for personalised medicine was infeasible [15].

The increasing use of targeted agents offers the great advantage of increased specificity and reduced toxicity when compared with conventional chemotherapy [16]. Meta-analysis in diverse tumour types has shown that a personalized strategy of treatment is an independent predictor of better outcomes and fewer toxicity associated deaths when compared with chemotherapy [17]. The recent advances in targeted next-generation sequencing (NGS) technologies optimized for nucleic acid templates extracted from FFPE tumour samples now provides the opportunity to conduct precision oncology testing as part of the routine diagnostic work-flow. To investigate the potential role of clinically directed semiconductor sequencing in solid tumours we have established a clinically validated NGS platform optimised for analysis of FFPE clinical biopsy samples. This platform enables detection of 867 druggable driver-partner oncogenic fusions via analysis of 51 driver and 349 partner genes, with linkage to 140 targeted therapy protocols. All variants detected are “actionable” and therefore treatable by targeted therapies either on-market FDA and EMA approved, carrying ESMO and NCCN guideline references or currently in clinical trials, phases I-IV, worldwide [18].

Here we have undertaken a retrospective analysis of test trending data to investigate the types and frequency of clinically relevant fusions in solid tumours. Here we show that semiconductor sequencing can be incorporated into routine pathology diagnostic work-flows enabling detection of druggable fusions at high frequency across many solid tumour types.

Materials and methods

Patient demographics

A retrospective analysis was performed on the trending data generated as part of routine comprehensive precision oncology NGS testing for solid tumours and collected in compliance with ISO15189:2012 requirements for monitoring of quality indicators. The research conducted in this study was limited to secondary use of information previously collected in the course of normal care (without an intention to use it for research at the time of collection) and therefore does not require REC review. The patients and service users were not identifiable to the research team carrying out trend data analysis. This is also in accordance with guidance issued by the National Research Ethics Service, Health Research Authority, NHS and follows the tenants of the Declaration of Helsinki. The trending data relates to a real-life cohort of 1112 patients tested between 14th February 2018 and 31st October 2019. The study cohort demographics are shown in S1 and S2 Tables. The cohort included all solid tumour types without application of inclusion or exclusion criteria and therefore representative of a larger population.

Comprehensive NGS genomic profiling

The NGS platform utilized for clinical testing is validated for clinical use and accredited by CLIA (ID 99D2170813) and by UKAS (9376) in compliance with ISO15189:2012 and following the guidelines published by the Association for Molecular Pathology and College of American Pathologists and IQN-Path ASBL as described [8, 19]. The performance characteristics of the assay are shown in S3 Table. The NGS platform includes the targeting of 51 driver genes and 349 partner genes, enabling detection of 867 druggable driver-partner oncogenic fusions that is linked to 140 targeted therapy protocols. Genomic regions selected for analysis of clinically relevant fusions are shown in S4 Table.

RNA extraction, library preparation and sequencing

RNA was extracted from FFPE curls cut at 10μm or from 5μm sections mounted onto unstained glass slides using the RecoverAll extraction kit (Ambion, Cat:A26069). RNA samples were diluted to 5ng/μl and reverse transcribed to cDNA in a 96 well plate using the Superscript Vilo cDNA synthesis kit (CAT 11754250). Library construction, template preparation, template enrichment and sequencing were performed using Ion Ampliseq library 2.0 (Cat: 4480441) and the Ion 540TM OT2 kit (Cat: A27753) according to the manufacturer’s instructions. Sequencing was performed using the Ion S5 system 20 (Cat: A27212) utilising Ion 540TM chips (Cat:30 A27766).

Quality control

Sequencing runs were quality controlled using the following parameters according to manufacturer’s instructions (Ion Reporter™ 5.10.1.0): chip loading >60% with >45 million reads observed, enrichment 98–100%, polyclonal percentage <55%, low quality <26%, usable reads > 30% and aligned bases were ≥80%, unaligned bases were <20%, mean raw accuracy was >99% and overall read length between 100-115bp, average base coverage depth >1200, uniformity of amplicon (base) coverage >90%, amplicons were required to have less than 90% strand bias with >80% of amplicons reading end to end, on-target reads >85% and target base coverage at 1x, 20x, 100x and 500x >90% (S5 Table).

Data analysis

Sequence alignment and variant calling was performed on The Torrent Suite™ Software (5.8.0). Alignment in Torrent Suite™ Software was performed using TMAP. The output BAM file was uploaded via the Ion Reporter Uploader plugin (5.8.32–1) to The Ion Reporter™ Software (5.10.1.0). Gene fusions were reported when occurring in >40 counts and meeting the threshold of assay specific internal RNA quality control with a sensitivity of 99% and PPV of 99%. Six internal expression quality controls were spiked into each sample to monitor assay performance with an acceptance cut-off of>15 reads in 5 out 6 controls [Ion Reporter™ 5.10.1.0; default fusion view 5.10] (S5 Table). The results of variant annotation were organized hierarchically by gene, alteration, indication and level of evidence in relation to clinical actionability following the joint recommendation of the association of the AMP/ASCO/CAP [8, 9]. Tertiary analysis software was used to link variants to curated lists of relevant labels, guidelines, and global clinical trials [Oncomine™ Reporter (Cat:A34298)]; GlobalData clinical trials database.

Results

Eighty nine actionable fusion gene events were identified in 1112 samples of solid tumours (Fig 1 and S6 Table). Eighty two of the 1112 samples tested had at least one actionable fusion gene representing a frequency of 7.4% across the study cohort. The frequency of the different actionable gene fusions/rearrangements detected is shown in Fig 1. TBL1XR1-PIK3CA, MET-MET, WHSC1L1-FGFR1, FGFR3-TACC3 and EGFR-SEPT14 fusions and EGFR VIII rearrangements were identified as the most common druggable events (Fig 1). Seven of the samples harboured two fusion genes. Four of these seven cases relate to glioblastoma in which fusion pairs CAPZA2-MET and FIP1L1-PDGFRA, CAPZA2-MET and MET-MET, EGFR VIII and PTPRZ1-MET, PTPRZ1-MET and TBL1XR1-PIK3CA were identified. Fusion pairs were also identified in colon (CAPZA2-MET and MET-MET), lung (PIK3CA-TBL1XR1 and RET-NCOA4) and pancreatic (FNDC3B-PIK3CA and TBL1XR1-PIK3CA) cancers (S6 Table).

Fig 1

Actionable fusion gene landscape in solid tumours.

The four most common fusions TBL1XR1-PIK3CA, MET-MET, WHSC1L1-FGFR1 and FGFR3-TACC3 representing 61% of all fusions were detected in a broad range of tumour types (Fig 2 and S6–S8 Tables). In contrast, some of the low frequency fusions, CCDC6-RET in thyroid and NSCLC lung cancer, FGFR2-BICC1 in cholangiocarcinoma, PTPRZ1-MET and KIF5B-RET in non-small cell lung (NSCLC) cancer (≤5 fusions detected) did show linkage to tumour type (Fig 1 and S6–S8 Tables). In tumour types with significant sample size (n >30), a high frequency (>7%) of actionable fusions was observed in glioblastoma, head and neck cancers, cancers of unknown primary (CUP), prostate and pancreatic cancers and in NSCLC (S8 Table). Glioblastoma, colorectal, lung and breast cancers harboured the most diverse set of actionable fusion genes (Fig 3). The fusions identified in the database were compared to those found in the TumorFusions data portal cataloguing over 20,000 gene fusions found in The Cancer Genome Atlas (TCGA) [20]. For gene fusions identified in both this study and the TCGA, there was little correspondence in the relative frequency of each fusion (S1 Fig). Fusions in the TCGA were also found in a variety of cancer types, for example the 36 FGFR3-TACC3 fusion events were spread across 10 different cancer types. No significant difference (p = 0.2) was found in the prevalence of fusions between primary (66/735, 9.0%) and metastatic (23/375, 6.1%) cancers and fusion read count showed no correlation with tumour percentage. In relation to read counts, liver cancer had a higher average when compared to colorectal cancer (p = 0.008), breast, pancreatic, sarcoma, endometrial, upper GI and thyroid cancers (0.01 < p < 0.05) using Tukey’s multiple comparisons test on the geometric means (S2 Fig). However, most cancer types had too few fusions to make statistical comparisons.

Fig 2

Frequency of the three most common actionable gene fusions in solid tumours.

Fig 3

Frequency of actionable gene fusions in glioblastoma, colorectal, NSCLC and breast cancers.

Analysis of fusions in relation to age and gender are shown in S2 and S9 Tables. Although no associations were observed with age, WHSC1L1-FGFR1 appears to be more prevalent in females (p = 0.004) contrasting with FGFR3-TACC3 which is more prevalent in males (p = 0.01).

Actionable fusions can be targeted directly by small molecule inhibitors or alternatively through targeting of their cognate downstream signalling pathways. Bioinformatics pathway analysis showed the actionable fusions identified in this study were linked to a number of evolutionary conserved druggable cell signalling pathways including RAS/RAF/MEK/ERK, PIK3/AKT/MTOR, PLCy/PKC, JAK/STAT and WNT/β catenin (Table 1). Detected fusions were bioinformatically linked to a total of 73 targeted therapy protocols. These included seven fusion-drug matches with FDA/EMA approval and/or NCCN/ESMO recommendations (Table 1) either in indication or approved in “cancer of other type” and therefore meeting the tier criteria I and II level of clinical significance as defined by the Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists [8]. These fusions were additionally linked to a total of 80 clinical trials investigating the efficacy of drugs either targeting the kinase fusion directly or alternatively using inhibitors targeting pathways downstream (Table 1).

Table 1

Actionable fusion genes in solid tumours.

			Relevant therapies	Clinical trials	Relevant Therapy
Genomic alteration	Cancer type	Pathway	*approval in other cancer type
KANK1—NTRK3	Breast	RAS/RAF/MEK/ERK	entrectinib	NCT02576431	larotrectinib
		PI3K/AKT/MTOR pathway	larotrectinib	NCT02637687	larotrectinib
		PLCy/PKC		NCT02568267	entrectinib
			*Larotrectinib (all solid tumours)	NCT02465060	larotrectinib
				NCT03155620	larotrectinib
				NCT02920996	merestinib
				NCT03213704	larotrectinib
				NCT03297606	temsirolimus
				No NCT ID	entrectinib
				NCT03093116	repotrectinib
				NCT03215511	LOXO-195
FGFR1—NRG1	Breast	RAS/RAF/MEK/ERK		NCT02052778	TAS-120
		PI3K/AKT/MTOR pathway		NCT02393248	chemotherapy, INCMGA00012, pembrolizumab, pemigatinib, trastuzumab
		PLCy/PKC		NCT01948297	FF-284
		JAK/STAT		NCT03834220	FF-284
				NCT02272998	ponatinib
				NCT03297606	sunitinib
				NCT03160833	HMPL-453
				NCT03929965	anlotinib hydrochloride
				NCT03235570	pemigatinib
TMEM178B—MET	Colorectal	RAS/RAF/MEK/ERK		NCT03297606	crizotinib
		PI3K/AKT		NCT03175224	bozitinib
				NCT02219711	sitravatinib
EGFR—SEPT14	Colorectal, Glioblastoma	PI3K/AKT/MTOR pathway		No NCT ID	cetuximab + chemotherapy
		RAS/RAF/MEK/ERK		No NCT ID	cetuximab + chemotherapy
				NCT03152435	CART-EGFR
				NCT03454620	GC1118A + chemotherapy
				NCT03319459	cetuximab + FATE-NK100
				NCT03297606	erlotinib
				NCT02013089	erlotinib, gefitinib
				NCT02423525	afatinib
CAPZA2—MET	Glioblastoma, Colorectal	RAS/RAF/MEK/ERK		NCT03598244	volitinib
		PI3K/AKT		NCT03297606	crizotinib
				NCT03175224	bozitinib
				NCT02219711	sitravatinib
FIP1L1—PDGFRA	Glioblastoma	RAS/RAF/MEK/ERK		NCT03352427	dasatinib, everolimus
		PI3K/AKT/MTOR pathway
PTPRZ1—MET	Glioblastoma	RAS/RAF/MEK/ERK		NCT03598244	volitinib
		PI3K/AKT		NCT03297606	crizotinib
				NCT03175224	bozitinib
				NCT02219711	sitravatinib
AGK—BRAF	Glioblastoma	RAS/RAF/MEK/ERK		NCT02639546	cobimetinib
				NCT02029001	sorafenib
				NCT03843775	binimetinib + encorafenib
				NCT03520075	ASTX029
				NCT03905148	lifirafenib, PD-0325901
				NCT03415126	ASN007
				NCT02857270	abemaciclib, cetuximab, chemotherapy, encorafenib, LY3214996, midazolam
				NCT02607813	LXH254
				NCT03634982	RMC-4630
TBL1XR1—PIK3CA	Breast, Cervical, Colorectal, CUP, endometrial, gastric, glioblastoma, head and neck, GOJ, NSCLC, Oesophageal, Ovarian, Pancreatic, Thyroid, Vulva	PI3K/AKT/MTOR pathway		NCT03292250	alpelisib
				NCT03065062	gedatolisib + palbociclib
				NCT03297606	temsirolimus
				NCT02576444	capivasertib, olaparib
				NCT03673787	atezolizumab + ipatasertib
				NCT03805399	chemotherapy, mTOR inhibitor
				NCT02615730	GSK-2636771 + chemotherapy
				NCT03675893	abemaciclib + LY-3023414
FGFR3—TACC3	Glioblastoma, Head and Neck and prostate	RAS/RAF/MEK/ERK	*erdafitinib (Bladder cancer)	NCT03292250	nintedanib
		PI3K/AKT/MTOR pathway		NCT01948297	FF-284
		JAK/STAT		NCT03834220	FF-284
				NCT02272998	ponatinib
				NCT03297606	sunitinib
				NCT02052778	TAS-120
				NCT03160833	HMPL-453
				NCT03929965	anlotinib hydrochloride
				NCT03235570	pemigatinib
WHSC1L1—FGFR1	Breast Cancer, CUP, Renal Cell Carcinoma, Oesophageal, Ovarian, Sarcoma, Small bowel	RAS/RAF/MEK/ERK		NCT02872714	pemigatinib
		PI3K/AKT/MTOR pathway		NCT02052778	TAS-120
		JAK/STAT		NCT03160833	HMPL-453
		PLCy/PKC		NCT03834220	FF-284
				NCT02272998	ponatinib
				NCT03297606	sunitinib
				NCT01948297	FF-284
				NCT03929965	anlotinib hydrochloride
				NCT03235570	pemigatinib
				NCT02393248	chemotherapy, INCMGA00012, pembrolizumab, pemigatinib, trastuzumab
				NCT03822117	pemigatinib
				NCT02052778	futibatinib
FGFR2—BICC1	Cholangiocarcinoma (Liver)	RAS/RAF/MEK/ERK	*erdafitinib (Bladder cancer)	NCT03230318	derazantinib
		PI3K/AKT/MTOR pathway		NCT03773302	infigratinib
		JAK/STAT		NCT03656536	pemigatinib
				NCT02699606	erdafitinib
				NCT03834220	FF-284
				NCT02150967	infigratinib
				NCT02924376	pemigatinib
				NCT02691793	sunitinib
				NCT02272998	ponatinib
				NCT03297606	sunitinib
				NCT02052778	TAS-120
				NCT02393248	chemotherapy, INCMGA00012, pembrolizumab, pemigatinib, trastuzumab
				NCT03160833	HMPL-453
				NCT01948297	FF-284
				NCT03929965	anlotinib hydrochloride
				NCT03235570	pemigatinib
KIF5B—RET	NSCLC	PI3K/AKT/MTOR	Cabozantinib	No NCT ID	alectinib
		RAS/RAF/MEK/ERK	vandetanib	NCT03194893	alectinib, crizotinib
		PLCy/PKC		NCT03391869	ipilimumab, nivolumab, radiation therapy, surgical intervention
				NCT01639508	cabozantinib
				NCT02314481	alectinib
				NCT03445000	alectinib
				NCT02540824	apatinib
				NCT02699606	erdafitinib
				NCT02299141	nintedanib
				NCT02664935	sitravatinib
				No NCT ID	targeted therapy, targeted therapy + chemotherapy
				NCT03157128	LOXO-292
				NCT03037385	BLU-667
				NCT03780517	BOS172738
				NCT02219711	sitravatinib
				NCT02029001	sorafenib
				NCT02450123	sunitinib
				NCT02691793	sunitinib
				NCT02272998	ponatinib
				NCT03297606	sunitinib
SND1—MET	NSCLC	RAS/RAF/MEK/ERK		NCT03088930	crizotinib
		PI3K/AKT		NCT02323126	capmatinib + nivolumab
				NCT02414139	capmatinib
				NCT02219711	sitravatinib
				NCT03297606	crizotinib
				NCT03175224	bozitinib
RET—NCOA4	NSCLC	PI3K/AKT/MTOR	cabozantinib	No NCT ID	alectinib
		RAS/RAF/MEK/ERK	vandetanib	NCT03194893	alectinib, crizotinib
		PLCy/PKC		NCT03391869	ipilimumab, nivolumab, radiation therapy, surgical intervention
				NCT02314481	alectinib
				NCT03445000	alectinib
				NCT02540824	apatinib
				NCT01639508	cabozantinib
				NCT02699606	erdafitinib
				NCT02299141	nintedanib
				NCT02664935	sitravatinib
				No NCT ID	targeted therapy, targeted therapy + chemotherapy
				NCT03157128	LOXO-292
				NCT03037385	BLU-667
				NCT03780517	BOS172738
				NCT02219711	sitravatinib
				NCT02029001	sorafenib
				NCT02450123	sunitinib
				NCT02691793	sunitinib
				NCT02272998	ponatinib
				NCT03297606	sunitinib
CCDC6—RET	NSCLC, Thyroid	PI3K/AKT/MTOR	cabozantinib (NSCLC)	No NCT ID	alectinib
		RAS/RAF/MEK/ERK	vandetanib (NSCLC)	NCT03194893	alectinib, crizotinib
		PLCy/PKC		NCT03391869	ipilimumab, nivolumab, radiation therapy, surgical intervention
				NCT02314481	alectinib
				NCT03445000	alectinib
				NCT02540824	apatinib
				NCT01639508	cabozantinib
				NCT02699606	erdafitinib
				NCT02299141	nintedanib
				NCT02664935	sitravatinib
				No NCT ID	targeted therapy, targeted therapy + chemotherapy
				NCT03157128	LOXO-292
				NCT03037385	BLU-667
				NCT03780517	BOS172738
				NCT02219711	sitravatinib
				NCT02029001	sorafenib
				NCT02450123	sunitinib
				NCT02691793	sunitinib
				NCT02272998	ponatinib
				NCT03297606	sunitinib
				NCT01945762	vandetanib
MET—MET	Bladder, CUP, Colorectal, Glioblastoma, NSCLC, Pancreatic cancer, Squamous cell carcinoma, Renal cell carcinoma	RAS/RAF/MEK/ERK	crizotinib (NSCLC)	NCT03297606	crizotinib
		PI3K/AKT/MTOR		NCT03175224	bozitinib
				NCT02219711	sitravatinib
				NCT03088930	crizotinib
				NCT03729297	cabozantinib
				NCT02323126	capmatinib + nivolumab
				NCT02213289	rilotumumab + chemotherapy
				NCT02414139	capmatinib
				NCT03598244	volitinib
				NCT02867592	cabozantinib
				NCT02465060	crizotinib
				NCT03911193	cabozantinib
				NCT02867592	cabozantinib
				NCT02465060	crizotinib
TMEM178B—BRAF	Teratoma (Other)	RAS/RAF/MEK/ERK		NCT02029001	sorafenib
				NCT03843775	binimetinib + encorafenib
				NCT03520075	ASTX029
				NCT02639546	cobimetinib
				NCT03905148	lifirafenib, PD-0325901
				NCT03415126	ASN007
				NCT02857270	abemaciclib, cetuximab, chemotherapy, encorafenib, LY3214996, midazolam
				NCT02607813	LXH254
				NCT03634982	RMC-4630
				NCT02013089	sorafenib, sunitinib
				NCT03714958	HDM-201 + trametinib
FNDC3B—PIK3CA	Pancreas	PI3K/AKT/MTOR pathway		NCT03065062	gedatolisib + palbociclib
				NCT03297606	temsirolimus
				NCT02576444	capivasertib, olaparib
				NCT03673787	atezolizumab + ipatasertib
SND1—BRAF	Prostate	RAS/RAF/MEK/ERK		NCT02029001	sorafenib
				NCT03843775	binimetinib + encorafenib
				NCT03520075	ASTX029
				NCT02639546	cobimetinib
				NCT03905148	lifirafenib, PD-0325901
				NCT03415126	ASN007
				NCT02857270	abemaciclib, cetuximab, chemotherapy, encorafenib, LY3214996, midazolam
				NCT02607813	LXH254
				NCT03634982	RMC-4630
BRAF—MRPS33	Prostate	RAS/RAF/MEK/ERK		NCT02029001	sorafenib
				NCT03843775	binimetinib + encorafenib
				NCT03520075	ASTX029
				NCT02639546	cobimetinib
				NCT03905148	lifirafenib, PD-0325901
				NCT03415126	ASN007
				NCT02857270	abemaciclib, cetuximab, chemotherapy, encorafenib, LY3214996, midazolam
				NCT02607813	LXH254
				NCT03634982	RMC-4630
EGFR—EGFR	Glioblastoma, Prostate	PI3K/AKT/MTOR pathway		NCT03297606	erlotinib
		RAS/RAF/MEK/ERK		NCT02423525	afatinib
				NCT03319459	cetuximab + FATE-NK100
PCM1—BRAF	Sarcoma	RAS/RAF/MEK/ERK		NCT02639546	cobimetinib
				NCT02029001	sorafenib
				NCT03843775	binimetinib + encorafenib
				NCT03520075	ASTX029
				NCT03905148	lifirafenib, PD-0325901
				NCT03415126	ASN007
				NCT02857270	abemaciclib, cetuximab, chemotherapy, encorafenib, LY3214996, midazolam
				NCT02607813	LXH254
				NCT03634982	RMC-4630
PTPRK—RSPO3	Colorectal	WNT/β catenin		NCT01351103	LGK974
EIF3E—RSPO2	Colorectal	WNT/β catenin		NCT01351103	LGK974

Discussion

Advances in somatic cancer genetics and genomic profiling is leading to a shift in treatment paradigm from relatively non-specific empirically directed cytotoxic chemotherapies to a more biologically informed targeted approach [21-23]. The drug-target pairing that links a dysregulated molecular pathway with a cognate therapeutic agent defines the modern era of precision oncology. Targeted agents and immunotherapies are associated with superior response rates, fewer side effects and reduced healthcare costs when compared with nonselective chemotherapy [16, 17, 24].

Analysis of our NGS test trending data has shown that actionable fusions occur across a wide range of tumour types providing more personalised treatment options to cancer patients. Actionable fusions were identified at high frequency, 7.3% across all solid tumour types, rising to 23% in the case of glioblastoma. Notably, high frequency actionable fusions were agnostic of tumour type in keeping with previous findings, for example the TCGA RNA sequencing (RNA-seq) data corpus, in which high frequency fusions were observed across multiple tumour types [20]. In contrast, less prevalent actionable fusions exhibited tumour type specificity including CCDC6-RET fusions in thyroid and lung cancer, FGFR2-BICC1 in cholangiocarcinoma, PTPRZ1-MET in glioblastoma, EIF3E-RSPO2 and PTPRK-RSPO3 in colorectal cancer and KIF5B-RET in NSCLC as previously reported [3, 25–28]. Other low frequency actionable fusions namely, AGK-BRAF, FIP1L1-PDGFRA, FNDC3B-PIK3CA, RET-NCOA4, SND1-BRAF, TMEM178B-MET, TMEM178B-BRAF, KANK1-NTRK3, EIF3E-RSPO2 and PTPRK -RSPO3 have been previously reported as rare fusions in tumours of other type [1, 28–34]. Non targeted novel fusions were also identified including FGFR1-NRG1 in breast, BRAF-MRPS33 in prostate, SND1-MET in lung, PCM1-BRAF in sarcoma and TMEM178B-MET in rectal cancer.

Detected actionable fusions were found to interact with one or more of the major evolutionary conserved cell signalling pathways, namely RAS/RAF/MEK/ERK, PI3K/AKT/MTOR, PLCy/PKC, JAK/STAT and WNT/β catenin. These are key signalling networks mediating fundamental processes including cell proliferation, differentiation, cell migration and apoptosis which are involved in homeostasis across all tissue types [35]. This may account for the fact that the most prevalent actionable fusions are independent of tumour type or tissue of origin. The minority population of tumour specific fusions identified also involve interaction with these conserved signalling networks suggesting that the tumour specificity of these particular fusions does not relate to tissue specific signalling pathways. Moreover, tissue specificity does not appear to be determined by tissue specific expression of the partner or driver genes involved in these fusions. For example, in the case of KIF5B-RET in NSCLC, KIF5B is expressed across a broad range of tissue types in keeping with its ubiquitous function as key component of the mitotic machinery and similarly RET is expressed across a broad range of tissue types [36, 37]. The mechanisms relating to tissue specificity remain poorly defined but may relate to other factors, for example interphase gene proximity (spatial proximity) that can facilitate generation of fusion genes [11, 38, 39]. Interestingly some fusions were associated with a particular tumour subtype, for example all fusions detected in breast cancer were restricted to cancers of ductal type and in lung cancers to those of NSCLC type. Cellular morphology has been postulated to represent a holistic readout of the complex genomic and gene expression changes in cancer cells and it is interesting to speculate that activation of the cognate signalling pathways linked to these particular fusions drive genomic changes and transcriptome profiles that act as determinants of these specific morpho-phenotypes [40].

The majority of detected druggable oncogenic driver genes represent tyrosine kinases (TKs) with partner genes encoding coil coiled domains leading to ligand-independent homodimerization, TK activation and dysregulated growth. However, it is also possible that disruption of the partner gene itself may also contribute to tumorigenesis through putative tumour suppressor roles. A number of the partner genes identified including TACC, CCDC6, EIF3E and KIF5B participate in DNA damage repair or mitotic chromosomal segregation [41-43]. Dysregulation of these genes have potential to drive error-prone DNA replication leading to genomic instability. This is in keeping with the notion that gene fusion events may function as a “two hit” model for multistep tumorigenesis.

Currently routine clinical molecular testing in relation to druggable fusion genes is limited to NSCLC, but even in this tumour type, analysis covers only a small number of potential driver and partner genes, namely ALK and ROS rearrangements [10]. Here we have shown that semiconductor sequencing analysis of fusion transcripts in routine FFPE samples applied across all solid tumour types enables comprehensive analysis of hundreds of druggable fusion genes. Their detection enables clinical evidence-based linkage to a broad therapeutic armamentarium of targeted therapies which can have a major impact in the improved clinical management of advanced solid tumours. The majority of targeted agents are directed against the fusions themselves but alternatively treatment protocols also include targeting components of the cognate signalling pathways downstream. For example, the TBL1XR1-PIK3CA fusion is linked to targeted therapies inhibiting PI3K directly (alpelisib) but also inhibitors targeting downstream signalling components including AKT (capivasertib), MTOR (temsirolimus) and CDK4/6 (abemaciclib). Although a targeted agent for TRK fusions now has FDA approval, this rearrangement was identified as a rare event in our cohort of solid tumours (<1%) in keeping with previous reports [1]. In contrast here we have shown that targeting high frequency fusions such as TBL1XR1-PIK3CA, MET-MET, WHSC1L1-FGFR1 fusions offer much broader clinical utility across all tumour types.

In summary, we have shown that NGS semiconductor sequencing configured for detection of druggable fusions identifies a high frequency of actionable genetic rearrangements in solid tumours and that their coverage is therefore an important component of comprehensive precision oncology profiling.. Notably, high prevalence actionable fusions are not tumour type specific reinforcing the “site agnostic” approach to genomic profiling and supporting the concept of “molecular basket” clinical trials. Importantly we have also demonstrated that adoption of semiconductor sequencing methodologies enables comprehensive precision oncology profiling to be applied robustly to routine FFPE clinical biopsy samples allowing integration with globally established routine diagnostic pathology workflows.

Ethical statement

The research conducted in this study was limited to secondary use of information previously collected in the course of normal care (without an intention to use it for research at the time of collection) and therefore does not require REC review. The patients and service users were not identifiable to the research team carrying out trend data analysis. This is also in accordance with guidance issued by the National Research Ethics Service, Health Research Authority, NHS and follows the tenants of the Declaration of Helsinki.

Number of incidences of each fusion event as a proportion of the total considering only fusions found in both the TCGA database and the Oncologica dataset.

(TIF)

Click here for additional data file.

Geometric mean of the number of fusions reads contained in each cancer type with 95% Wald confidence intervals.

** represents p < 0.01 and * < 0.05 by Tukey’s multipl comparisons test.

(TIF)

Click here for additional data file.

Cancer type and histological classification of the study cohort.

(PDF)

Click here for additional data file.

Study cohort demographics by primary vs metastatic disease, age and gender.

(PDF)

Click here for additional data file.

Assay performance characteristics.

(PDF)

Click here for additional data file.

Sequences of the 867 driver partner fusion events targeted by the Oncofocus test.

(PDF)

Click here for additional data file.

Quality control metrics.

(PDF)

Click here for additional data file.

Detected actionable gene fusions in the sample cohort (n = 1,112).

(PDF)

Click here for additional data file.

Low frequency gene fusions detected in the sample cohort.

(PDF)

Click here for additional data file.

Frequency of actionable gene fusion and cancer type.

(PDF)

Click here for additional data file.

Detected driver genes and fusions by gender and age.

(PDF)

Click here for additional data file.

11 Apr 2021

PONE-D-21-02472

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors

PLOS ONE

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Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #3: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This manuscript entitled “Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors” retrospectively evaluated 89 actionable fusions detected from 1112 patients with solid tumors. The authors analyzed genomic profiling, targeted therapies, and clinical trials of the identified fusions. This manuscript is well written and below are the comments.

Major comments:

1. The authors conclude that the majority of detected actionable fusions are independent of tumor type or tissue of origin. However, Table 1 and Figure 2 shows that there are specific fusions only detected in specific tumor types, such as EGFR VIII in glioma and prostate cancer. In addition, other retrospective studies have concluded that the frequency of fusions identified is dependent on specific tumor type (e.g. Pavalan et al., 2019). The authors should provide other data/evidences to support this conclusion.

2. In addition, this study identified 51 driver genes in the driver-partner fusions. It would be more comprehensive if the authors could include additional analysis of actionable variants of these driver genes to further conclude the association between actionable fusions and the tumor types.

3. Figure 1 and Figure 2 provide frequency of the actionable gene fusions in solid tumors, is it possible to provide a summary table of the molecular findings separated by age and gender?

4. The manuscript only includes the 89 actionable fusion gene identified. Can the author also provide a summary table of the other fusions detected in different tumor types?

Minor comments:

1. Second and third paragraphs of the discussion sections can be moved to the result section.

2. Type and writing errors in introduction and discussion section (e.g. line 226, 228)

3. There are many fusion databases available (e.g. TCGA fusion), the authors should consider comparing the fusion distribution from this study to the databases.

Reviewer #2: This is an interesting study to demonstrate the frequency of fusions in solid tumors and the importance and opportunity for expanded precision medicine in patient treatment. The manuscript is well written and does provide a good basis of information however it can be expanded further for impact and significance.

Specific comments:

1. Expansion on the cohort:

a. Overall information for whole group such as, tumors originated in females or males, age demarcation, tumor type is primary or metastatic, etc.

b. Further cohort delineation in regard to whether the cohort was screened for other immunohistochemical status or molecular testing such as SNVs or MNVs. Are the identified fusions seen in SNV negative samples or equally? Etc.

c. Outcome information for any potential correlation to fusion containing tumors and disease outcome

2. Methods section clarifications are needed to ensure the ability to replicate the data. In addition, in supplementary tables 2 and 3, Oncofocus test is mentioned but not described at all in the methods or other sections.

3. Fusion read count could be expanded, and any correlation between tumor types, tumor percentage and fusion read count could be valuable.

4. The figures focus on frequency however adding the number of cases that had the specific fusions to the charts would add to the figures.

Reviewer #3: 4/3/2021

Dear Dr., Reddi,

Thank you for inviting me to review the original research article titled “Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors” by Loddo et al.

This is a well-written manuscript emphasizing the importance of fusion gene analysis and precision medicine in the NGS era. Especially, the discussion section of the manuscript covered and linked an array of interesting topics. I do not see any major flaws in the manuscript, and I recommend that this manuscript can be accepted for publication once addressing the following suggestions.

Comments

1) Line 34 has a typo with an extra “W” before the word “Where”.

2) In Line 65, the sentence starting with “This platform enables..” can be rephrased as “This platform enables detection of 867 druggable driver-partner oncogenic fusions via analysis of 51 driver and 349 partner genes, with linkage to 140 targeted 68 therapy protocols”.

3) Line 96 can be rephrased as “ The NGS platform includes the targeting of 51 driver genes and 349 partner genes enabling detection of 867 druggable driver-partner oncogenic fusions that is linked to 140 targeted therapy protocols.”

4) In Lines 137 and 141 – I would not call EGFRvIII as a gene fusion event, but it is a gene rearrangement.

5) In Line 228 – there is a typo “ keeping wFith”

6) Line 231 – The first sentence can be rephrased.

Thanks,

Pavalan

**********

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Submitted filename: Reviewer Comments_Fusions.docx

Click here for additional data file.

27 Dec 2021

Dear Dr Reddi,

Many thanks for your email. We have now addressed your requests and wish to submit the revised manuscript.

We look forward to your reply.

Best Wishes

Dr Marco Loddo

BSc PhD

Professor Gareth H Williams

BSc MBChB PhD FRCPath FLSW

PONE-D-21-02472R1

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors Dr Marco Loddo

Dear Dr. Loddo,

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We have included the following paragraph at the end of the manuscript:

“The authors received no specific funding for this work. Data analysis conducted in this study was limited to secondary use of information previously collected in the course of normal care. No dedicated funding source was allocated for this study. The funder provided support in the form of salaries for all authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.” Pages 19-20, lines 272-277.

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I have read the journal's policy and the authors of this manuscript have the following competing interests: Competing interests

ML and GW are shareholders and directors of Oncologica UK Ltd. ML, KH, TH, RT and GW are currently employed at Oncologica UK Ltd.

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We have clarified these points in the above paragraph which has been inserted at the end of the manuscript. Pages 19-20, lines 272-277.

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1) The values behind the means, standard deviations and other measures reported;

Values reported in Results for fusion frequencies, metastatic status and fusion read counts (page 7) derived from raw data in Supplementary Table 6, summarised for each cancer type in Supplementary Table 8. Age and gender data (page 8) is located in Supplementary Tables 2 and 9. Table 1 contains data used to report targeted therapy protocols (page 8).

2) The values used to build graphs;

Figures 1, 2 and 3 contain raw values in legend, with data derived from Supplementary tables 6 and 8.

3) The points extracted from images for analysis.

N/A

8 Feb 2022

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

Honey V. Reddi

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Dear Dr. Loddo - While some of your responses have been accepted by our reviewers,  reviewer 1 has noted that you have not provided a specific response to their earlier comments and it is important that those comments be addressed.

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Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

**********

6. Review Comments to the Author

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Reviewer #1: The authors did not response to the comments below.

This manuscript entitled “Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors” retrospectively evaluated 89 actionable fusions detected from 1112 patients with solid tumors. The authors analyzed genomic profiling, targeted therapies, and clinical trials of the identified fusions. This manuscript is well written and below are the comments.

Major comments:

1. The authors conclude that the majority of detected actionable fusions are independent of tumor type or tissue of origin. However, Table 1 and Figure 2 shows that there are specific fusions only detected in specific tumor types, such as EGFR VIII in glioma and prostate cancer. In addition, other retrospective studies have concluded that the frequency of fusions identified is dependent on specific tumor type (e.g. Pavalan et al., 2019). The authors should provide other data/evidences to support this conclusion.

2. In addition, this study identified 51 driver genes in the driver-partner fusions. It would be more comprehensive if the authors could include additional analysis of actionable variants of these driver genes to further conclude the association between actionable fusions and the tumor types.

3. Figure 1 and Figure 2 provide frequency of the actionable gene fusions in solid tumors, is it possible to provide a summary table of the molecular findings separated by age and gender?

4. The manuscript only includes the 89 actionable fusion gene identified. Can the author also provide a summary table of the other fusions detected in different tumor types?

Minor comments:

1. Second and third paragraphs of the discussion sections can be moved to the result section.

2. Type and writing errors in introduction and discussion section (e.g. line 226, 228)

3. There are many fusion databases available (e.g. TCGA fusion), the authors should consider comparing the fusion distribution from this study to the databases.

Reviewer #3: Thank you authors for your efforts to include my suggestions in the manuscript. I am certain that this manuscript is acceptable for publication.

**********

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17 May 2022

23 August 2021

Dear Dr Reddi,

Many thanks for your email. We have now address the referee’s comments and wish to submit the revised manuscript.

Please find below a point by point response to the referee’s comments as requested.

We look forward to your reply.

Best Wishes

Professor Gareth H Williams

BSc MBChB PhD FRCPath FLSW

Dr Marco Loddo

BSc PhD

2. In your Methods section, please provide additional information about the participant selection method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as:

a) the date you accessed the patient data. This is recorded in the material and methods section, page 4, lines 88-89. We have now added the precise dates to further clarify the temporal aspects of the cohort.

b) a description of any inclusion/exclusion criteria that were applied to participant selection. No inclusion or exclusion criteria were applied, analysis was conducted on all tumour types tested between the specified dates

c) a table of relevant demographic details. A sentence has been inserted in the materials and methods section, page 4, line 89 “The demographics of the cohort are summarized in Supplementary Table 1 and 2”.

d) a statement as to whether your sample can be considered representative of a larger population.

The cohort included all solid tumour types without application of inclusion or exclusion criteria and therefore representative of a larger population. This sentence has been included in the revised manuscript, pages 4, 5, lines 89-91.

Reviewer #1: This manuscript entitled “Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors” retrospectively evaluated 89 actionable fusions detected from 1112 patients with solid tumors. The authors analyzed genomic profiling, targeted therapies, and clinical trials of the identified fusions. This manuscript is well written and below are the comments.

We are grateful for the supportive comments on our manuscript.

Major comments:

1. The authors conclude that the majority of detected actionable fusions are independent of tumor type or tissue of origin. However, Table 1 and Figure 2 shows that there are specific fusions only detected in specific tumor types, such as EGFR VIII in glioma and prostate cancer.

As highlighted in the results section page 7 lines 140-148 the high prevalence fusions TBL1XR1-PIK3CA, MET-MET and WHSC1L1-FGFR1 are independent of tumour types whereas the lower prevalence fusions (around the 6% cut point) are restricted to certain tumour types in keeping with previous studies (Table 1, Figure 2 and Supplementary Table 6). The referee is entirely correct in identifying the confusing sentence “the majority of detected actionable fusions are independent of tumour type” which is present in the abstract (page 2, line 22) and last paragraph of the discussion (page 18, line 256) and grateful for picking up this critical point. These three genes constitute the “majority” of actionable fusion events but represent only a minority (not majority) of all fusion types detected. Once the prevalence reaches around 6% the fusions start to show tumour specificity. The author is also correct that EGFR VIII is showing tumour specificity in relation to glioma and prostate cancer and should not be included in Figure 2 but Supplementary Table 6. This has been amended. Moreover FGFR3-TACC3 which has been included in supplementary table 6 actually shows distribution across multiple tumour types including Head & Neck, prostate and glial tumours. To address these critical points we have made the following changes to the manuscript

1) We have moved EGFR VIII to Supplementary Table 7

2) We have removed EGFR VIII from Figure 2. We have replaced EGFR VIII with FGFR3-TACC3 in the result section, page 7, lines 152- 154

3) We have corrected the sentences which include “majority of fusions ….. with “more prevalent/most prevalent, pages 2, line 22; page 17, line 202; page 17, 218; page 19, line 261

In addition, other retrospective studies have concluded that the frequency of fusions identified is dependent on specific tumor type (e.g. Pavalan et al., 2019). The authors should provide other data/evidences to support this conclusion.

The pavalan et al., 2019 study was a screen of all fusions including actionable, unknown significance and benign. Only 7 were classified as actionable over a cohort of 183 tumour samples and therefore it is difficult to compare the data with our study which focuses exclusively on actionable fusions

1) We cite the TCGA RNA sequencing (RNA-seq) data corpus to support our findings that high prevalence fusions are agnostic of tumour type for example the 36 FGFR3-TACC3 fusion events were spread across 10 different cancer types, page 8, lines 160-163.

2) The results and discussion section cites publications relating to tumour specific fusions in keeping with our results. These are referenced in the paper (24-35)

2. In addition, this study identified 51 driver genes in the driver-partner fusions. It would be more comprehensive if the authors could include additional analysis of actionable variants of these driver genes to further conclude the association between actionable fusions and the tumour types.

The assay platform targets 505 genes and detects actionable genetic variants linked to 780 anti-cancer targeted therapies/therapy combinations. This includes the 51 driver genes relating to the fusions analysed in this study. We are presently analysing all variants CNVs, deletions, indels, SNVs etc across the cohort. This is a complex pan-cancer big data study is beyond the scope of the present manuscript. We will however be citing the data relating to fusions in the forthcoming manuscript and collating with other somatic variants including mutually exclusive or concurrent genetic alterations to determine pathway interactions.

3. Figure 1 and Figure 2 provide frequency of the actionable gene fusions in solid tumours, is it possible to provide a summary table of the molecular findings separated by age and gender?

We have now conducted this analysis and the following data inserted into the manuscript.

Analysis of fusions in relation to age and gender are shown in Supplementary Table 2 and 9. Although no associations were observed with age, WHSC1L1-FGFR1 appears to be more prevalent in females contrasting with FGFR3-TACC3 which is more prevalent in males (page 8, lines 170-172; supplementary table 2 and 9)

4. The manuscript only includes the 89 actionable fusion gene identified. Can the author also provide a summary table of the other fusions detected in different tumour types?

The linkage between fusion type and tumour type are summarised in Supplementary Tables 6-8. This is a targeted NGS assay designed to detect 867 druggable driver-partner oncogenic fusions via analysis of 51 driver and 349 partner genes, with linkage to 140 targeted therapy protocols. This is not a screening assay and therefore non actionable fusions e.g. unknown significance, benign were not identified, page 5, line 101-103.

Minor comments:

1. Second and third paragraphs of the discussion sections can be moved to the result section.

With insertion of additional content in response to referees comments we feel on balance that these paragraphs are best placed in the discussion

2. Type and writing errors in introduction and discussion section (e.g. line 226, 228)

Thank you for highlighting these errors. These have now been amended.

3. There are many fusion databases available (e.g. TCGA fusion), the authors should consider comparing the fusion distribution from this study to the databases.

We have now compared our data to fusion data databases as requested including correlation of fusion read counts with tumour type and percentage. This has been incorporated into the results section and discussion section, page 8, lines 167-171 and page 17, lines 202- 204.

Reviewer #2: This is an interesting study to demonstrate the frequency of fusions in solid tumors and the importance and opportunity for expanded precision medicine in patient treatment. The manuscript is well written and does provide a good basis of information however it can be expanded further for impact and significance.

We are most grateful for the supportive comments on our manuscript.

Specific comments:

1. Expansion on the cohort:

a. Overall information for whole group such as, tumors originated in females or males, age demarcation, tumor type is primary or metastatic, etc.

We agree these are important associations and have conducted the appropriate analysis. This data has now been incorporated into Supplementary Table 2 and results section, page 8, lines 172-174.

b. Further cohort delineation in regard to whether the cohort was screened for other immunohistochemical status or molecular testing such as SNVs or MNVs. Are the identified fusions seen in SNV negative samples or equally? Etc.

These are interesting questions including whether loss of DDR function and associated genomic instability might show linkage with fusions. The assay platform targets 505 genes and detects actionable genetic variants linked to 780 anti-cancer targeted therapies/therapy combinations. This includes the 51 driver genes relating to the fusions analysed in this study. We are presently analysing all variants CNVs, deletions, indels, SNVs etc across the cohort. This is a complex pan-cancer big data study which is beyond the scope of the present manuscript. We will however be citing the data relating to fusions in the forthcoming manuscript and collating with other somatic variants including mutually exclusive or concurrent genetic alterations to determine pathway interactions.

c. Outcome information for any potential correlation to fusion containing tumours and disease outcome

This study is a retrospective analysis of test trending data relating to our clinical service. Unfortunately our data is restricted to pathological information only and does not include outcome data.

2. Methods section clarifications are needed to ensure the ability to replicate the data. In addition, in supplementary tables 2 and 3, Oncofocus test is mentioned but not described at all in the methods or other sections.

Oncofocus was mentioned in error as it refers to the brand name of the assay. This has now been corrected.

3. Fusion read count could be expanded, and any correlation between tumor types, tumor percentage and fusion read count could be valuable.

Fusion read count in relation to tumour type and percentage has now been incorporated into the manuscript, page 8, lines 167-171.

4. The figures focus on frequency however adding the number of cases that had the specific fusions to the charts would add to the figures.

Case numbers have now been included in the Figures.

Reviewer #3: 4/3/2021

Dear Dr., Reddi,

Thank you for inviting me to review the original research article titled “Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors” by Loddo et al.

This is a well-written manuscript emphasizing the importance of fusion gene analysis and precision medicine in the NGS era. Especially, the discussion section of the manuscript covered and linked an array of interesting topics. I do not see any major flaws in the manuscript, and I recommend that this manuscript can be accepted for publication once addressing the following suggestions.

Comments

1) Line 34 has a typo with an extra “W” before the word “Where”.

This has been corrected.

2) In Line 65, the sentence starting with “This platform enables..” can be rephrased as “This platform enables detection of 867 druggable driver-partner oncogenic fusions via analysis of 51 driver and 349 partner genes, with linkage to 140 targeted 68 therapy protocols”.

We have inserted the suggested sentence, page 4, line 68-70.

3) Line 96 can be rephrased as “ The NGS platform includes the targeting of 51 driver genes and 349 partner genes enabling detection of 867 druggable driver-partner oncogenic fusions that is linked to 140 targeted therapy protocols.”

We have inserted the suggested sentence, page 5, line 101-103.

4) In Lines 137 and 141 – I would not call EGFRvIII as a gene fusion event, but it is a gene rearrangement.

We have replaced EGFR VIII fusion with rearrangement, page 7, line 141, 142.

5) In Line 228 – there is a typo “ keeping wFith”

This has been corrected.

6) Line 231 – The first sentence can be rephrased.

This sentence has been rephrased.

23 August 2021

Dear Dr Reddi,

Many thanks for your email. We have now addressed your requests and wish to submit the revised manuscript.

We look forward to your reply.

Best Wishes

Dr Marco Loddo

BSc PhD

Professor Gareth H Williams

BSc MBChB PhD FRCPath FLSW

PONE-D-21-02472R1

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors Dr Marco Loddo

Dear Dr. Loddo,

We've checked your submission and before we can proceed, we need you to address the following issues:

1, Thank you for stating the following financial disclosure:

N/A

At this time, please address the following queries:

1a) Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution.

1b) State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

1c) If any authors received a salary from any of your funders, please state which authors and which funders.

1d) If you did not receive any funding for this study, please state: “The authors received no specific funding for this work.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

We have included the following paragraph at the end of the manuscript:

“The authors received no specific funding for this work. Data analysis conducted in this study was limited to secondary use of information previously collected in the course of normal care. No dedicated funding source was allocated for this study. The funder provided support in the form of salaries for all authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.” Pages 19-20, lines 272-277.

2. Thank you for providing the following Funding Statement:

I have read the journal's policy and the authors of this manuscript have the following competing interests: Competing interests

ML and GW are shareholders and directors of Oncologica UK Ltd. ML, KH, TH, RT and GW are currently employed at Oncologica UK Ltd.

We note that one or more of the authors is affiliated with the funding organization, indicating the funder may have had some role in the design, data collection, analysis or preparation of your manuscript for publication; in other words, the funder played an indirect role through the participation of the co-authors.

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section.”

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Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

We have clarified these points in the above paragraph which has been inserted at the end of the manuscript. Pages 19-20, lines 272-277.

3. We note that your Data Availability statement states the following: "The datasets analysed during the current study are provided in the Supplementary File. Further technical details are available from the corresponding author upon reasonable request."

PLOS journals require that all data presented in the study be made publicly available at or before the time of publication. If there are legal or ethical restrictions on the data being made publicly available, such as IRB restriction or patient confidentiality, authors must provide a way for fellow researchers to access the data.

At this time, please confirm that your submission contains your "minimal data set", which PLOS defines as consisting of the data set used to reach the conclusions drawn in the manuscript with related metadata and methods, and any additional data required to replicate the reported study findings in their entirety. This includes:

We can confirm that the submission contains the minimal dataset.

1) The values behind the means, standard deviations and other measures reported;

Values reported in Results for fusion frequencies, metastatic status and fusion read counts (page 7) derived from raw data in Supplementary Table 6, summarised for each cancer type in Supplementary Table 8. Age and gender data (page 8) is located in Supplementary Tables 2 and 9. Table 1 contains data used to report targeted therapy protocols (page 8).

2) The values used to build graphs;

Figures 1, 2 and 3 contain raw values in legend, with data derived from Supplementary tables 6 and 8.

3) The points extracted from images for analysis.

N/A

23 August 2021

Dear Dr Reddi,

Many thanks for your email. We have now addressed your requests and wish to submit the revised manuscript.

We look forward to your reply.

Best Wishes

Dr Marco Loddo

BSc PhD

Professor Gareth H Williams

BSc MBChB PhD FRCPath FLSW

PONE-D-21-02472R1

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors Dr Marco Loddo

Dear Dr. Loddo,

We've checked your submission and before we can proceed, we need you to address the following issues:

1, Thank you for stating the following financial disclosure:

N/A

At this time, please address the following queries:

1a) Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution.

1b) State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

1c) If any authors received a salary from any of your funders, please state which authors and which funders.

1d) If you did not receive any funding for this study, please state: “The authors received no specific funding for this work.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

We have included the following paragraph at the end of the manuscript:

“The authors received no specific funding for this work. Data analysis conducted in this study was limited to secondary use of information previously collected in the course of normal care. No dedicated funding source was allocated for this study. The funder provided support in the form of salaries for all authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.” Pages 19-20, lines 272-277.

2. Thank you for providing the following Funding Statement:

I have read the journal's policy and the authors of this manuscript have the following competing interests: Competing interests

ML and GW are shareholders and directors of Oncologica UK Ltd. ML, KH, TH, RT and GW are currently employed at Oncologica UK Ltd.

We note that one or more of the authors is affiliated with the funding organization, indicating the funder may have had some role in the design, data collection, analysis or preparation of your manuscript for publication; in other words, the funder played an indirect role through the participation of the co-authors.

If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study in the Author Contributions section of the online submission form. Please make any necessary amendments directly within this section of the online submission form. Please also update your Funding Statement to include the following statement: “The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’

section.”

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Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

We have clarified these points in the above paragraph which has been inserted at the end of the manuscript. Pages 19-20, lines 272-277.

3. We note that your Data Availability statement states the following: "The datasets analysed during the current study are provided in the Supplementary File. Further technical details are available from the corresponding author upon reasonable request."

PLOS journals require that all data presented in the study be made publicly available at or before the time of publication. If there are legal or ethical restrictions on the data being made publicly available, such as IRB restriction or patient confidentiality, authors must provide a way for fellow researchers to access the data.

At this time, please confirm that your submission contains your "minimal data set", which PLOS defines as consisting of the data set used to reach the conclusions drawn in the manuscript with related metadata and methods, and any additional data required to replicate the reported study findings in their entirety. This includes:

We can confirm that the submission contains the minimal dataset.

1) The values behind the means, standard deviations and other measures reported;

Values reported in Results for fusion frequencies, metastatic status and fusion read counts (page 7) derived from raw data in Supplementary Table 6, summarised for each cancer type in Supplementary Table 8. Age and gender data (page 8) is located in Supplementary Tables 2 and 9. Table 1 contains data used to report targeted therapy protocols (page 8).

2) The values used to build graphs;

Figures 1, 2 and 3 contain raw values in legend, with data derived from Supplementary tables 6 and 8.

3) The points extracted from images for analysis.

N/A

27 December 2021

Dear Dr Reddi,

Many thanks for your email. We have now addressed your requests and wish to submit the revised manuscript.

We look forward to your reply.

Best Wishes

Dr Marco Loddo

BSc PhD

Professor Gareth H Williams

BSc MBChB PhD FRCPath FLSW

PONE-D-21-02472R1

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors Dr Marco Loddo

Dear Dr. Loddo,

We've checked your submission and before we can proceed, we need you to address the following issues:

1. Thank you for providing additional information. However, in order to provide our readers with as much transparency as possible, PLOS ONE policy requires that certain statements be present in the competing interests statement when authors have commercial affiliations.

At this time, we would like to propose the following updated funding disclosure statement to appear alongside your published paper:

"The authors received no specific funding for this work. Data analysis conducted in this study was limited to secondary use of information previously collected in the course of normal care. No dedicated funding source was allocated for this study. The funder provided support in the form of salaries for all authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.” Pages 19-20, lines 272-277."

I confirm this paragraph has already been inserted in the previous submission.

We would also like to propose the following updated competing interest statement to appear alongside your published paper:

"The authors have read the journal's policy and the authors of this manuscript have the following competing interests: Authors ML, KH, AL, RT, and GW are currently salaried employees at Oncologica UK Ltd. TH was a salaried employee at the time of data generation for this manuscript. There are no patents, products in development, or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials."

The competing interest section has now been amended as requested

Please confirm or amend this statement to proceed. We hope to hear from you soon.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

25 Jul 2022

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors

PONE-D-21-02472R2

Dear Dr. Loddo,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Honey V. Reddi

Academic Editor

PLOS ONE

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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11 Aug 2022

PONE-D-21-02472R2

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumours

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