TITLE α-particle irradiation - Cancer Research · 1 TITLE: Cellular and genetic determinants of...

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TITLE: Cellular and genetic determinants of the sensitivity of cancer to α-particle irradiation AUTHORS: Brian D. Yard1, Priyanka Gopal1, Kristina Bannik2, Gerhard Siemeister2, Urs B. Hagemann2, Mohamed E. Abazeed1,3

AFFILIATIONS: 12111 East 96th St/NE6-258, Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, OH, 44195; 2Muellerstrasse 170–178, Berlin D-13342, Germany, Research & Development, Pharmaceuticals, Bayer AG; 310201 Carnegie Ave/CA-60, Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, 44195. *To whom correspondence should be addressed: Mohamed E. Abazeed Cleveland Clinic Phone: 216-445-0061 2111 East 96th St Fax: 216-636-2498 NE6-315 E-mail: [email protected] Cleveland, OH 44195 Running title: High-content α-particle irradiation platform Key words: radiogenomics, precision medicine, PI3K, Her2 Word Count: 5492 Figures: 6 Supplementary Tables: 4 Supplementary Figures: 5 Conflict of Interest Notification: MEA discloses a research grant, travel support and an honorarium from Bayer AG. The other authors of this study have no conflicts of interest pertaining to the conduct of this research.

on June 26, 2020. © 2019 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on August 6, 2019; DOI: 10.1158/0008-5472.CAN-19-0859

http://cancerres.aacrjournals.org/

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ABSTRACT

Targeted α-particle emitting radionuclides have great potential for the treatment of a broad range

of cancers at different stages of progression. A platform that accurately measures cancer cellular

sensitivity to α-particle irradiation could guide and accelerate clinical translation. Here, we

performed high-content profiling of cellular survival following exposure to α-particles emitted

from radium-223 (223Ra) using 28 genetically diverse human tumor cell lines. Significant

variation in cellular sensitivity across tumor cells was observed. 223Ra was significantly more

potent than sparsely ionizing irradiation, with a median relative biologic effectiveness of 10.4

(IQR: 8.4-14.3). Cells that are the most resistant to γ radiation, such as Nrf2 gain-of-function

mutant cells, were sensitive to α-particles. Combining these profiling results with genetic

features we identified several somatic copy number alterations, gene mutations, and the basal

expression of gene sets that correlated with radiation survival. Activating mutations in PIK3CA,

a frequent event in cancer, decreased sensitivity to 223Ra. The identification of cellular and

genetic determinants of sensitivity to 223Ra may guide the clinical incorporation of targeted α-

particle emitters in the treatment of several cancer types.




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SIGNIFICANCE

Findings address limitations in the preclinical guidance and prediction of radionuclide tumor

sensitivity by identifying intrinsic cellular and genetic determinants of cancer cell survival

following exposure to α-particle irradiation.




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INTRODUCTION

The linear accelerator, the most commonly used device in clinical radiotherapy practices,

generates sparsely ionizing radiation in the form of X-rays or electrons [1]. The penetrant but

dispersed ionization tracks are maneuvered to conform to the shape of the target tumors using

multiple incident beams that superpose to effect tumor cell death [2]. However, ionization tracks

rely on a confluence of variables to confer death. Tumor cells sustain lethal damage only when

two or more lesions take place within one or two helical turns of a DNA strand [3-5]. The

frequency of clusters of damage also vary based on the orientation of the DNA, its

compactness and the probabilistic trajectories of the ionization tracks [6]. Contributing to the

uncertainty, the genetic variation across and within distinct cancer types has been shown to

modulate the risk of tumor cellular death either by mitigating damage or facilitating its repair

[7]. The mere conditional ability of sparsely ionizing radiation to cause lethal DNA damage is

reflected in the overall incomplete clinical local control across several cancer types [8].

Shortly after the discovery of the X-ray in 1895, Marie and Pierre Curie described the

activity of radium and its more potent physiologic properties compared to X-rays [9]. Short

exposures of radium to the skin produced inflammation that exhibited similar effects to those

obtained after much longer exposure to X-rays. This suggested that radium’s emitted particles

are substantially more potent than sparsely ionizing X-rays. It is now evident that short-range

charged α-particles emitted by radium induce clustered DNA damage along their tracks,

resulting in significantly more effective cell death per unit of absorbed dose [10-12]. Despite

the qualitatively observed potency, the exact relative biological effectiveness of α-particles

compared to sparsely ionizing radiation across a panel of cancer cells remains unknown [13, 14]




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and it is unclear whether physical (e.g. cellular size, shape, and nuclear volume) or cancer

genetic variables can modulate the survival of tumors to this more potent form of radiation.

The long half-life (~1600 years) of radium’s most stable isotope, radium-226, precluded

radium’s medical use for over a century. However, an artificially generated isotope with a much

shorter half-life, radium-223 (223Ra, half-life = 11.4 days), has been effectively incorporated into

routine cancer therapeutic use. 223Ra, the first α-emitter approved by the US Food and Drug

Administration, takes advantage of radium’s bone mimetic properties for the treatment of

patients with castrate resistant prostate cancer with bone metastases [15, 16]. The established

clinical efficacy of 223Ra has led to significant interest in expanding the use of α-particles to

target cancers other than those that are localized in bone. The treatment of extra-skeletal cancers

can be achieved through the use of targeted radionuclide therapy, which use molecular carriers

with high affinity to antigens on the surface of tumor cells [17-21]. The ability to target α-

emitting particles to visceral disease is poised to improve response rates across a range of

cancers, including appreciably more radiation resistant solid tumors.

Despite the potential for the use of targeted α-emitting radionuclides in solid tumors,

very little is known about the interplay between α-particles and tumor cell sensitivity. To date,

there have not been extensive analyses of the cellular vulnerability to α-particle radiation within

or across cancer types. Although inhibition of DNA double-strand break repair has been shown

to sensitize cancer cells to α-particle treatments [22, 23], suggesting that the composition of the

cancer genome could regulate sensitivity, or a genetic basis for response to α-particles across

distinct tumors, has yet to be established. An improved understanding of the relationship

between the sensitivity of tumors to α-particles and their cellular and genetic characteristics can

more appropriately inform their future application. These could include the tailoring of α-particle




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prescriptions and schedules, biomarker-guided patient selection and/or employing more precise

drug/α-particle treatments.

Here, we develop an integrated imaging, microdosimetric, cellular and genomic high-

content platform that measures the survival across a diverse panel of tumor cells following

exposure to α-particles and leverages cancer genetic data for biomarker identification.




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METHODS

Cell culture and irradiation. Cell lines from the Cancer Cell Line Encyclopedia (CCLE) were

authenticated per CCLE protocol [24] and grown in recommended media supplemented with

10% fetal bovine serum (ThermoFisher, MA) and 100 U/mL Penicillin, 100 µg/mL of

Streptomycin, and 292 µg/mL L-Glutamine (Corning, NY). Immortalized bronchial epithelial

BEAS-2B cells were purchased from ATCC and maintained in Advanced DMEM/F12 media

(ThermoFisher, MA) supplemented with 1% fetal bovine serum and 100 U/mL Penicillin, 100

µg/mL of Streptomycin, and 292 µg/mL L-Glutamine (Corning, NY). All cultures were

maintained at 37o C in a humidified 5% CO2 atmosphere and tested to ensure absence of

Mycoplasma. Plates were treated with γ-radiation delivered at 0.85 Gy/min with a 137Cs source

using a GammaCell 40 Exactor (Best Theratronics; Ontario, Canada) or radium-223 dichloride

(223RaCl2). The specific activity of 223Ra is 1.9 MBq/ng. The six-stage-decay of 223Ra to lead-207

(207Pb) occurs via short-lived daughters, and is accompanied by a number of α, β and γ emissions

with different energies and emission probabilities. The fraction of energy emitted from 223Ra and

its daughters as α-particles is 95.3% (energy range: 5.0 - 7.5 MeV). The fraction emitted as β-

particles is 3.6% (average energies are 0.445 MeV and 0.492 MeV), and the fraction emitted as

γ-radiation is 1.1% (energy range: 0.01 - 1.27 MeV).

High-throughput proliferation assay. Cells were plated using a Multidrop Combi liquid

handler (Thermo Fisher) in at least 6 replicates at a single previously determined optimal cell

density (range 30-1600 cells/well) in a white 96-well plate with opaque walls (Corning, NY).

Plates were irradiated with a single dose of 137Cs or a continuous dose of 223RaCl2 with

treatments delivered 24 hours after plating. Where indicated, hydroxyapatite (HA) (Sigma,




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H0252) was diluted (1:20,000) and added to the 100 μl of cell culture media at the time of

seeding. At 9-11 days post-irradiation, media was removed and 50 µL of CellTiter-Glo® reagent

(50% solution in PBS) (Promega, WI) was added to each well [25]. Relative luminescence units

(RLU) were measured using an Envision multilabel plate reader (Perkin Elmer) with a

measurement time of 0.1 seconds. Luminescence signal is proportional to the amount of ATP

present. The luminescence signal was plotted as a function of cell density and a cell density

within the linear range for luminescence (or growth) was selected to generate integral survival

values for each cell line.

Clonogenic survival. Cells were plated at appropriate dilutions, irradiated, and incubated for 7-

21 days for colony formation. Colonies were fixed in a solution of acetic acid and methanol 1:3

(v/v) and stained with 0.5% (w/v) crystal violet as previously described [26]. A colony was

defined to consist of 50 cells or greater. Colonies were counted digitally using ImageJ software

as described [27].

Integral survival and RBE. The integral area under the curve was estimated by trapezoidal

approximation. The survival values for each trapezoid were multiplied by the dose interval,

[f(X1)+f(X2)/2]*∆X, and summed. To avoid differences in RBE along the shape of the dose

response curves (e.g. D50, D10, or Do), values were calculated as the ratio of (AUCγ/AUCα) for

each cell line. To relate our estimates with RBE values reported by others, we also calculated

RBE using D37.




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Microscopy. Tumor cells were plated in a 96-well half area high content imaging glass bottom

microplate (Corning, NY) at a density of 500-2000 cells/well. Wells were pretreated using poly-

D-Lysine (Sigma) 2 hours before cellular plating. Cells were then fixed and permeabilized using

the Image-iT™ Fixation/Permeabilization Kit (ThermoFisher, MA). After fixation, the cytosol

and nuclei were stained using actin green probes (green) (ThermoFisher, MA) and Hoechst

DAPI (blue) or Propidium Iodide (red) (ThermoFisher, MA), respectively. For each tumor cell

line, at least 2 wells and 4 images per well (8 images in total) were captured at 4x magnification

using a Cytation 1 cell imaging multi-mode reader (BioTek). All images were processed

manually using the ImageJ software. To measure the diameter of single cells (rather than clusters

of cells), a threshold was set for the minimum and maximum pixel area size to exclude clusters

of cells. Estimates of cytoplasmic and nuclear diameter were made by circular or ellipsoid fitting,

outlining the chosen pixel area based on fluorescence intensity. The average cytoplasmic and

nuclear diameters (the average of the major and minor axes in the case of ellipsoid fitting) of at

least 100 cells per tumor cell line were calculated and their radii were used as input for

microdosimetric calculations.

Cellular microdosimetry. In the decay chain of 223Ra, a total of four high-energy daughter α-

particles (219Rn, 215Po, 211Bi, and 211Po) and two beta decays (211Pb and 207Tl) are generated,

with 207Pb as the final stable end product. We used the microdosimetry schema proposed by

Roeske and Stinchcomb for calculations of absorbed α-particle dose, D, in Gy, for given α-parti-

cle energies, our source location, and individual target sizes [28]. The values for the input

quantities are calculated such that 𝐷𝐷 = 𝑛𝑛 𝑥𝑥 𝑧𝑧1, where n is the average number of hits to the target

and 𝑧𝑧1 is the average of the single-hit specific energy (energy deposited per unit mass). 𝑧𝑧1 values




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were either explicitly tabulated or obtained by linear interpolation for each cellular and nuclear

radii pair. For the source-target geometry where the source is the medium outside the cell, its

volume was taken to be one cm3 with the target cell at the center. This choice for the source

volume allows for using the cumulated activity per cm3 such that S is equal to the absorbed dose

per unit of cumulated activity (Gy•cm3/Bq•s). We used 𝑆𝑆 = 10−12𝜋𝜋(𝑧𝑧1) �𝑅𝑅𝛼𝛼𝑟𝑟2 +

(23)�𝑟𝑟3 + (𝑠𝑠2 − 𝑟𝑟2)3/2 − 𝑠𝑠3��, where 𝑅𝑅𝛼𝛼is the range of the alpha particle and s and r are cellular

and nuclear radii, respectively. In our system, all of our cells adhered to the bottom of the well

so we applied a correction of 0.5 to the S-value to take into account the exclusively hemispheric

irradiation (only from the top). In the presence of hydroxyapatite, the correction was not required

due to the spherical distribution of irradiation; the cells are fully immersed in the matrix. For

cells that had an ellipsoid shape, a 0.9 correction was applied. This correction is derived from the

relationship between S and electron energy for an ellipsoid geometry and is based on an average

particle range of ~60 µm and its corresponding electron energy. We used 𝐷𝐷 = 𝐴𝐴 𝑥𝑥 𝑆𝑆 to calculate

the absorbed dose for each α-particle. The cumulated activity for 223Ra at initial activity 𝐴𝐴0 was

calculated using 𝐴𝐴 = 𝐴𝐴0𝑒𝑒−𝜆𝜆𝜆𝜆, where 𝜆𝜆 = −0.693/𝑇𝑇ℎ𝑎𝑎𝑎𝑎𝑎𝑎−𝑎𝑎𝑙𝑙𝑎𝑎𝑙𝑙, and integrated for the duration of

exposure. The cumulated activity for each α-particle emitting daughter was calculated using

𝐴𝐴2 = � 𝜆𝜆2𝜆𝜆2− 𝜆𝜆1

�𝐴𝐴0(𝑒𝑒−𝜆𝜆1𝜆𝜆 − 𝑒𝑒−𝜆𝜆1𝜆𝜆 ), where 𝜆𝜆2 is the decay constant for the daughter nuclide and

𝜆𝜆1 is the decay constant of 223Ra. We set the decay constant 𝜆𝜆1 to 223Ra for all daughters because

the activity of the daughters is short-lived and dependent on the half-life of 223Ra, which is

significantly greater than 219Rn, 215Po, and 211Bi (3.97 s, 1.78 ms and 2.14 min, respectively). In

addition, although there is some initial activity attributed to the daughters, we assume that the

daughter nuclides have zero activity at zero time. Since the half-life of 223Ra >> 219Rn, 215Po, and




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211Bi, the activity contributed by the daughters immediately upon cellular irradiation is negligible

compared to the overall accumulated activity.

Variant generation in lentiviral vectors. We performed mutagenesis in three steps: PCR, in

vitro recombination and transformation. Briefly, the gene ORF was PCR amplified by using

primers that contain incorporated mutated sequence. Fragments were then transferred directly to

the destination vector (pLX306 or pLEX307) by LR recombination (Invitrogen) and the

constructs were transformed into competent cells. The discontinuity at the mutation site was

repaired by endogenous bacterial repair mechanism. After virus infection (multiplicity > 1),

BEAS-2B cells were selected and maintained in the presence of 1 μg/mL puromycin.

Western blot analysis. Whole-cell lysates were made using M-PER lysis buffer (ThermoFisher,

MA). Proteins were separated on 4-12% bis-tris SDS PAGE gels with MOPs buffer and

transferred onto 0.45 µM nitrocellulose (ThermoFisher, MA). Blots were developed with ECL

Prime western blotting detection reagent (Amersham / GE Healthcare). Anti-PIKC3CA (clone

C73F8, #4249, 1:2000), anti-AKT (#9272, 1:2000), anti-phospho-S473-AK T (#9271, 1:1000),

anti-HER2 (clone D8F12, #4290, 1:2000), anti-phospho-Y1248-HER2 (#2247, 1:1500), and

anti-β-actin (clone 13E5, #4970, 1:5000) were from Cell Signaling Technology (Danvers, MA).

Information-based association score. The association between genomic alterations (e.g.

mutations or somatic copy number alteration [SCNA]) or single-sample Gene Set Enrichment

Analysis (ssGSEA) profiles for each gene set and the radiation response profile was determined

using the Information Coefficient (IC) [25, 29, 30].




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Genetic data. Cancer cell lines were profiled at the genomic level and processed as described in

detail [24]. The processed data is available for download at http://www.broadinstitute.org/ccle.

Briefly, mutation information was obtained by using massively parallel sequencing of exomes.

Genotypes were transformed to categorical values (mutation = 1, no mutation = 0) and were used

as input to compute the IC.

Genotyping/copy-number analysis was performed using Affymetrix Genome-Wide

Human SNP Array 6.0. Raw Affymetrix CEL files were converted to a single value for each

probe set representing a SNP allele or a copy number probe using a GenePattern pipeline[31] and

hg18 Affymetrix probe annotations. Copy numbers were then inferred based upon estimating

probe set specific linear calibration curves, followed by normalization by the most similar

HapMap normal samples. Segmentation of normalized log2 ratios (specifically, log2(CN/2)) was

performed using the circular binary segmentation (CBS) algorithm [32], followed by median

centering of the segment values to a value of zero in each sample. Next, quality checking of each

array was performed, including visual inspection of the array pseudo-images, probe-to-probe

noise variation between copy-number values, confidence levels of Birdseed genotyping calls,

and appropriate segmentation of the copy-number profiles [33]. Finally, the Genomic

Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify focal

regions of copy number alterations in individual samples [34]. A gene-level copy number was

also generated, defined as the maximum absolute segmented value between the gene’s

coordinates, and calculated for all genes using the hg18 coordinates provided by the refFlat and

wgRna databases from UCSC Genome Browser



http://www.broadinstitute.org/ccle


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(http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/). Separate binary variables

representing amplifications (above 0.7) and deletions (below -0.7) were generated based on the

GISTIC gene-level copy number output described above. These binary amplification/deletion

variables for each gene were used as input to compute the IC against the radiation sensitivity

phenotype.

mRNA gene expression was measured by RNAseq. RPKM values was used as input to

calculate the ssGSEA enrichment scores based on the weighted difference of the Empirical

Cumulative Distribution Functions of the genes in the set relative to the genes not included in an

individual set [35]. The result is a single score per cell line per gene set, transforming the original

dataset into a more interpretable higher-level description. Gene sets were obtained from the C2

sub-collection of the Molecular Signatures database (MSigDB), an additional collection of

oncogenic signatures, and other cancer-related gene sets curated from the literature, resulting in a

dataset that has 5,826 pathway profiles for each sample [36]. ssGSEA values were used as input

to compute the IC.

The nominal p-values for the information-based association metric scores between the

genetic parameters (alterations or ssGSEA scores) and radiation response scores were estimated

using an empirical permutation test.



http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/


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RESULTS

Development and validation of a high-content α-particle irradiation survival platform

We profiled 28 cancer cell lines comprising 10 cancer types by modifying an established high-

throughput profiling platform for cellular survival after γ irradiation [7, 25]. To profile the

response of cancer cells to α-particles in a format amenable to high-throughput profiling, we first

determined the proliferating fraction (mean RLU at dose x/mean RLU of control) as a function of

dose for all cell lines using optimized growth measurements in a 96-well plate (Fig. 1a). We

calculated the integral survival value for each cell line and compared it to the corresponding

value after single fraction exposure to sparsely ionizing γ (137Cs) irradiation (Supplementary Fig.

1). We showed that although cell lines that were more resistant to γ irradiation were generally

more likely to be less sensitive to α-particles; 223Ra was more effective in inducing cellular

growth delay and/or death in all 28 cell lines (Fig. 1b&c). There were notable exceptions to the

overall correlation above and below the trendline. These, in part, included less sensitive cells

including two classes of small cell lung carcinoma with neuroepithelial and mesenchymal

differentiation (DMS53 and DMS114) and a breast carcinoma cell line with ERBB2

amplification, AR overexpression and a canonical PIK3CA mutation (HCC202). Interestingly,

these and other relatively more resistant cells had a longer cellular doubling time (Pearson r =

0.60, R2 = 0.35, and P = 0.03; Supplementary Fig. 2). We tested the impact of the duration of

exposure to α-particle on the relative sensitivity of cells with short (HSC4, RERFLCAI, and

HCT15) or longer doubling times (LNCaP and BT474). Reduction of the exposure time to 24

hours, with time to readout at 8 to 10 days after washout, did not significantly alter the relative

sensitivity of the five cell lines (Supplementary Fig. 3). These data indicate that the relationship

between doubling time and radiation sensitivity is not merely related to the experimental design




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and that cellular repopulation is not a major determinant of decreased sensitivity up to 10 days

after radiation treatments.

We next examined whether the high-throughput platform correlated with clonogenic

survival following exposure to 223Ra. Mean integral survival values for 12 cell lines (for each cell

line, n ≥ 2) was calculated and compared with values from the clonogenic assay (Fig. 1d).

Proliferation and colony integral survival values were significantly correlated, with Pearson r =

0.78, R2 = 0.61, and P = 0.003. Therefore, the high-throughput platform accurately profiles

cancer cell lines for response to radium and demonstrated significant variation in survival across

and within several cancer types.

α-particle microdosimetry and cellular morphology

In our experimental system, our targets (nuclei and cytoplasms) receive dose from medium

outside of the cell from 223Ra and its daughters over 9 to 11 days of continuous exposure. In this

setting of cells irradiated in a uniform solution of α-particles, the activity of the medium alone is

insufficient to estimate the absorbed dose for each tumor cell line. First, the average absorbed

dose across a panel of cells with distinct cellular and nuclear dimensions, and therefore target

size, is likely to vary. Second, since the range of the α-particle is on the same order of magnitude

as the diameter of the target (e.g. the nucleus), not all cells of a particular cell line will receive

the same dose. Therefore, a dosimetric framework that can account for inter- and intra-cell line

variation in absorbed dose estimates is essential to interpret biological results.

To accurately estimate the average absorbed dose, we first calculated the average specific

energy deposited to each tumor cell based on the individual nuclear and cytoplasmic dimensions

(i.e. radii). We used fluorescence capture of nuclear and cytoplasmic staining to calculate the




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dimensions of each tumor cell line (Fig. 2a). Although we found some variation in the radial

dimension and ploidy of tumor cell nuclei, the variation was significantly greater for cytoplasms

compared to nuclei, σ2 = 3.2 v. 0.7, respectively (F-test, P = <0.001) (Fig. 2b). With these

parameters as input, we calculated the average absorbed dose in each cell line from all α-particle

emissions (223Ra, 219Rn, 215Po, and 211Bi) (Fig. 2c). We showed that the activity (in kBq) is

highly correlated with the absorbed dose (in Gy) across the 28 cell lines (R2 = 0.97, P = <0.001)

(Fig. 2d). Linear regression showed a slope of 2.4 ± 0.1, reflecting the lower areas under the

curve after adjusting for absorbed dose.

We also correlated integral survival values using the calculated absorbed dose with

cellular parameters across the 28 cell lines. Cells with larger cytoplasmic, but not nuclear, radii

appeared to correlate with integral survival (Supplementary Fig. 4a&b). Specifically, cells with

larger cytoplasmic radii were more likely to be sensitive to α-particles. Correspondingly,

nuclear-to-cytoplasmic ratios were also associated with radiation sensitivity (R2 = 0.5, p <

0.0001) (Supplementary Fig. 4c). These results suggested that cellular shape and morphology

may impact cancer survival after α-particle irradiation.

The osteomimetic hydroxyapatite and the cellular response to α-particles

Hydroxyapatite (HA) is a calcium phosphate similar to the human bone in morphology and

composition insofar as it has a hexagonal structure and a similar stoichiometric calcium-to-

phosphate ratio [37]. HA has been previously shown to induce osteogenic differentiation from

cellular precursors [38] and alter the transcriptome and proteome of co-cultured cells [39]. Since

223Ra exerts its effect by incorporation into the bone, we sought to assess the impact of HA on

cellular sensitivity to α-particles using a co-incubation method (Fig. 3a). First, we measured the




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cellular survival as a function of the initial activity of 223Ra in the medium without or with HA

(Fig. 3b). Both the GI50 (compare Fig. 1a and Fig. 3b) and the integral survival values were

significantly reduced by the addition of HA (Fig. 3c). This suggested that 223Ra effectively bound

to the HA matrix and was more effective in this source geometry per unit of activity than as an

element in suspension. We then calculated the absorbed dose and considered the source

geometry after exchange between calcium and 223Ra. In this system, irradiation occurs from both

the top and bottom of the cell (no hemispheric correction [see Methods]). After adjusting for the

source geometry, there was no difference in the overall sensitivity of cells to α-particles without

or with HA (Fig. 3d).

Relative biological effectiveness of α-particles

We also compared the efficacy of α-particle irradiation to that of sparsely ionizing

radiation (137Cs). For each cell line, we used 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐴𝐴𝐴𝐴𝐴𝐴𝛼𝛼𝐴𝐴𝐴𝐴𝐴𝐴𝛾𝛾

to calculate the relative biologic

effectiveness of α-particles relative to γ irradiation. The median RBE, calculated using area under

the curve estimates, was 10.4 [IQR: 8.4-14.3]. The median RBE, calculated using D37, was 9.7

[IQR: 4.5-12]. These results indicate that α-particles are significantly more effective in tumor

cell killing than γ-rays.

Unlike α-particle emissions, sparsely ionizing radiation causes DNA damage mostly

through intermediary reactive oxygen species (ROS). Nrf2 and its binding partner Keap1 are key

regulators of oxidative stress response [40, 41]. We previously identified mutations in the Nrf2

pathway as some of the most highly correlated with resistance to γ irradiation [7, 25].

Accordingly, we sought to assess the relative importance of the oxidative response pathway in

regulating survival after α-particle irradiation. Cell lines with Nrf2 (NFE2L2) or KEAP1




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mutations were significantly more sensitive to α-particle treatments than γ irradiation and were

among some of the most sensitive cells to these treatments (Fig. 4a and Supplementary Table 1).

To study the effects of Nrf2 directly, we expressed Nrf2 wild type and T80K in an

immortalized human bronchial epithelial cell line (BEAS-2B). Mutation T80K has previously

been shown to abrogate binding to Keap1, resulting in activation of the Nrf2 pathway [42]. We

showed that overexpression T80K led to an increase in Nrf2 protein level, followed by wild type

and then vector control cells (Fig. 4b). Correspondingly, Nrf2 T80K proteins, and to a lesser

extent wild type, significantly increased the resistance of BEAS-2B cells to γ irradiation from a

137Cs source (Fig. 4c). In contrast, BEAS-2B cells expressing either T80K or wild type Nrf2 did

not demonstrate significantly different survival measurements compared to vector alone cells

after α-particle irradiation. We note that T80K mutations, and to a lesser extent wild type Nrf2,

contributed to marginally improved survival after α-particle irradiation compared to vector

control despite not achieving statistical significance (Supplementary Fig. 5).

Altogether, these results quantify the relative potency of α-particles and demonstrate that

they can effectively lethally damage cells that are the most resistant to sparsely ionizing radiation

at much lower Gray values.

Integrative genetic profiling of the cellular response to α-particles

We observed significant variation in survival across cell lines, on the order of a 32-fold

difference between the most sensitive and resistant cells (Fig. 5a). The variation in α-particle

response juxtaposed with the genetic heterogeneity of the profiled cell lines suggested that

genetic parameters may regulate response to α-particle irradiation. We sought to identify these

putative associations. We used single-sample Gene Set Enrichment Analysis (ssGSEA)




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projections as a gene set identification tool to find genetic pathways that are differentially

correlated with radiation response (see Methods) (Fig. 5b) [25, 35]. We also identified gene

mutations and somatic copy number alterations (SCNA) that correlated with radiation sensitivity

across all cell lines (Fig. 5c&d). The recent availability of whole exome sequencing data from

the Cancer Cell Line Encyclopedia (CCLE) significantly broadened our association analysis

from ~1600 genes to ~19,000 [43].

We compared the profiles of each gene set with the α-particle radiation response scores

(integral survival). The ssGSEA scores are displayed in a heatmap with the top gene sets that

correlate with radiation survival organized by biologic annotation (Fig. 5b, Supplementary Table

2). The top gene sets that correlated with reduced radiation sensitivity revealed pathways

associated with breast cancer, cellular signaling and hypoxia. Within the breast cancer category,

estrogen signaling, the luminal B subtype and ERBB2 signaling were associated with reduced

sensitivity. Interestingly, several individual gene sets within the breast category demonstrate that

subtypes with a propensity to metastasize to the bone are less likely to be sensitive to α-particles

(e.g. SMID_RELAPSE_IN_BONE_UP).

To assess the association of individual SCNA with radiation response, we correlated

alterations with radiation survival using the IC (Fig. 5c and Supplementary Table 3). Consistent

with the association in ERBB2 cell signaling by ssGSEA, ERBB2 amplification was also

associated with decreased sensitivity to α-particle treatments. In fact, ERBB2 amplification

scored second out of 46,637 potential gene-level SCNA associations (IC = 0.592, P =<0.001).

Consistent with these results, overexpression of ERBB2 has previously been shown to confer

therapeutic resistance and Trastuzumab, a monoclonal antibody that interferes with ErbB2,




20

sensitizes ErbB2 expressing cells to sparsely ionizing radiation[44].

PIK3CA variants reduced cellular sensitivity to α-particle irradiation

To assess the association of individual mutations with radiation response, we correlated

mutations with radiation survival using the IC (Fig. 5d and Supplementary Table 4). We

associated whole exome data, which included sequencing data from 18,750 genes, with cellular

survival to 223Ra. We identified mutations in PIK3CA as one of the top genes associated with

decreased sensitivity to irradiation (IC = 0.402, P = 0.03), which ranked 29th overall. We mapped

the individual PIK3CA mutations on a linear protein coordinate and delineated its domains (Fig.

6a). E545K, E542K and H1047R were well represented in the profiled cell lines, implicating

functionally relevant and frequent mutations in PIK3CA since all three mutations have been

previously shown to activate the enzyme [45].

We sought to examine the impact of activating mutations in PIK3CA on cellular survival

after α-particle irradiation without the confounding effects of varied background genetic

alterations in the profiled human cancer derived cell lines. To achieve this, we expressed

PIK3CA E545K and H1047R in the same genetically defined, immortalized human bronchial

epithelial cell line, BEAS-2B. PIK3CA variants and not wild type led to constitutive activation of

the phosphoinositide 3-kinase (PI3K)/AKT signaling pathway (Fig. 6b). PIK3CA variant

expressing cells had a distinct cellular morphology compared to wild type expressing and vector

control cells (Fig. 6c). Importantly, although the nuclear-to-cytoplasmic ratios did not vary

substantially based on the genotype, we accounted for the larger nuclear and cytoplasmic radii in

PIK3CA mutant and, to a lesser extent, wild type cells compared to cells with vector alone in our

calculation of the absorbed dose of 223Ra. Both PIK3CA E545K and H1047R, but not wild type,




21

enhanced cellular survival to α-particle irradiation (Fig.6d). We evaluated H1047R using both

proliferation profiling (Fig. 6e) as well as colony formation assays to confirm these findings (Fig.

6f&g). These results indicated that activating mutations in PIK3CA decrease the sensitivity of

cells to α-particle irradiation.




22

DISCUSSION

We developed and benchmarked a high-content platform that measures 223Ra radiation survival

across a diverse panel of tumor cells. The platform integrates fluorescence microscopy,

individual cellular microdosimetry and computational approaches that span the preclinical

experimental continuum from measuring radiation sensitivity to associating cancer genetic data

with treatment responses. We used this platform to profile 28 cancer cell lines for response to

223Ra and estimated the absorbed dose to each cell line. Critically, the dose range in which we

observed a dose response to α-particle treatments across multiple cell lines (the steep aspect of

the survival curves) significantly overlapped with clinical estimates of the mean absorbed dose to

metastatic tumors in bone after Ra223 treatments (0.2-1.9 Gy) [46-48]. These findings, coupled

with the expected significant stochastic variations of energy deposited within small targets,[49]

suggests that survival measurements from our platform are likely to be clinically relevant.

We showed that α-particle treatments were significantly more effective than sparsely

ionizing radiation with an RBE of ~10, which is two-fold higher than its previously estimated

magnitude [13, 14]. These results are consistent with the proposed direct action of the incident α-

particle on DNA and the attenuated cross-resistance to γ irradiation with α-particles compared to

the converse [50]. Coupled with approaches to estimate the RBE for normal tissues,[51] more

individualized estimates of RBE based on genomic biomarkers could guide clinical dose

schedules for α-emitting radionuclides.

Despite the increased sensitivity of cells to α-particles, the distribution of integral

survival measurement across the panel of cells suggested significant underlying cellular and

genetic diversity. Specifically, a trend in association was observed between nuclear-to-

cytoplasmic ratios and responses to α-particles. This associations appears to be mainly driven by




23

cytoplasmic size, with larger cells demonstrating higher sensitivity to treatments. There is

evidence to support a modest contribution toward DNA damage from cytoplasmic irradiation

[52, 53]. However, the magnitude of the effect that we observed suggests a more substantial

association between cytoplasmic irradiation and survival. It is unclear whether this association is

regulated by energy deposition by an α-particle produced outside of the cell or whether

continuous exposure with 223Ra allows for intracellular sequestration via exchanges with similar

elements (i.e. calcium, magnesium, iron, and/or copper). For example, there is some evidence for

sequestration of 223Ra by intracellular ferritin [54].

We studied the effects of bony matrix on cellular sensitivity. We found that the

radioactivity required to effect iso-sensitivity was significantly less when cells were grown in the

presence of bone-like HA matrix in vitro, indicating that there was exchange between the HA

(calcium) and 223Ra. However, adjustment for the source geometry resulted in a corrected

absorbed dose of irradiation that indicated no significant differences in sensitivity. These results

indicate that our measurement of cellular sensitivity is not altered when cells are placed in a

matrix that mimics bone. The implication of these findings is that candidate biomarkers

identified through our profiling effort can potentially direct the use of α-particles to treat tumors

that are located in the bone or the viscera.

We identified several gene expression set determinants of response to α-particles. The

identification of breast cancer cells overall and mainly those with a propensity to travel to bone,

the luminal B subtype,[55] as markers of decreased sensitivity may inform clinical studies of

223Ra in patients with metastatic breast cancer. We also identified genetic alterations that can

potentially have predictive capacity by identifying the likelihood of response to treatments. A

subset of these alterations (e.g. ERBB2 amplification) can potentially guide combinatorial




24

therapeutic strategies since these alterations both conferred decreased sensitivity and are targets

of approved drugs. Lastly, the oxygen enhancement ratio (OER) of high linear energy transfer

(LET) particles is predicted to be substantially less than sparsely ionizing radiation. Nonetheless,

the LET of α-particles is in the range 60-110 keV/µm resulting in an OER that remains >1 [56].

This, coupled with the biological effects of hypoxia beyond oxygen fixation of DNA damage,

suggests that hypoxic tumors may remain relatively more resistant to α-particles than their non-

hypoxic counterparts as indicated in our gene set associations.

Importantly, we demonstrated that cells that are among the most resistant to γ radiation

can be effectively treated with α-particles. The largest radiation cell line profiling effort

conducted to date revealed that cells with alterations in oxidative stress response, namely Nrf2

and its binding partner Keap1, are highly associated with γ radiation resistance [7]. Our results,

both in cancer cell lines and immortalized human bronchial epithelial cells made to express an

activating allele of Nrf2, indicate that α-particles can effectively kill these cells. These results

can impact future strategies of radiation dose escalation in tumors with a preponderance of

NRF2/KEAP1 alterations. Those include tumors of the head and neck, esophagus, lung and

bladder [57].

We validated our genetic biomarker platform by demonstrating that PIK3CA activating

mutations confer decreased sensitivity to α-particles. This finding is critical since it demonstrates

that some cancer cells, despite continuous and uniform irradiation, show decreased sensitivity to

high LET irradiation. Moreover, these results demonstrate that survival is guided by genetic

alterations, indicating that biologically guided radiotherapy will also be relevant for α-particle

treatments. The high frequency of PI3K pathway alterations across several cancer types presents




25

an opportunity for the combinatorial targeting of tumors with both α-particle and PI3K

inhibitors.

In summary, we have established a platform designed for biomarker and target

identification to α-particle treatments. Our strategies could guide appropriate patient selection

for these treatments and result in the improved clinical outcomes for patients with tumors that are

putatively the most resistant to sparsely ionizing radiation treatments.




26

END NOTES

M.E.A. was supported by NIH KL2TR0002547, NIH R37CA222294 and VeloSano. MEA

discloses research grant support from Siemens Healthcare and research grant, travel support and

honorarium from Bayer AG. The other authors declare no conflicts of interest. B.D.Y. and P.G.

assisted with experimental study conception and design, conducted and analyzed the

experimental work and edited the manuscript. K.B. contributed detailed working knowledge on

the hydroxyapatite co-culturing methodology. U.H. and G.S. assisted with data interpretation and

edited the manuscript. M.E.A. assisted with the design and analysis of the experimental work and

conceived, designed, conducted, analyzed and interpreted the computational work, including

cellular α-particle microdosimetry. M.E.A. supervised the experimental and computational work

and wrote the manuscript.




27

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31

FIGURE LEGENDS

Figure 1. High-throughput profiling (HTP) of cellular survival after α-particle irradiation. (a)

The surviving fraction after treatment with 223Ra for all 28 cell lines. Dose represents the initial

activity of 223Ra. Data are expressed as the means ± s.e.m. (b) Integral survival of dose (in kBq)

was calculated for each cell line profiled by the high-throughput platform after treatment with

223Ra and compared with integral survival values of the corresponding cell lines after treatment

with γ-irradiation (137Cs). Data points represent the mean of at least two experiments. The

diagonal represents the iso-sensitivity delimiter. (c) Integral survival of dose (in kBq) after 223Ra

and 137Cs treatments were plotted as box and rotated kernel density plots. The p-value of

Wilcoxon test for paired values was <0.001 an indicated by *. (d) Integral survival of dose (in

Gy) after 223Ra treatment profiled by the high-throughput platform (HTP) and by colony

formation assay (CFA) were analyzed by linear regression and the R2 value was calculated. Data

points represent the mean of at least two experiments.

Figure 2. High-content cellular microdosimetery for 223Ra treatments. (a) The cytosol and nuclei

for each cell line were stained green and blue, respectively. (b) Histogram and probability

density function of measured radii for all 28 cell lines. (c) Schematic depiction of cells exposed

to 223Ra and α-particle products along its decay chain. The range for the path-lengths of the 4

particles produced is shown. “t =10” days indicated the average number of days of α-particle

incubation with cells. (d) Integral survival calculated from 223Ra initial radioactivity

measurements (kBq) were plotted against values calculated from 223Ra absorbed dose (Gy).




32

Figure 3. Hydroxyapatite and cellular survival after α-particle irradiation. (a) Schematic

depiction of cells co-cultured with hydroxyapatite. (b) The surviving fraction of cells co-cultured

with hydroxyapatite after 223Ra treatment. Dose represents the initial activity of 223Ra. Data are

expressed as the means ± s.e.m. (c) Integral survival values calculated from 223Ra initial

radioactivity measurements (kBq) were plotted with and without hydroxyapatite for each cell

line. (d) Integral survival values calculated from 223Ra absorbed dose (Gy) measurements were

plotted with and without hydroxyapatite for each cell line.

Figure 4. Nrf2 mutant cells are effectively treated with α-particle irradiation. (a) Integral

survival values of cell lines with Nrf2 (NFE2L2) or KEAP1 mutations after 223Ra and 137Cs

treatments are shown. Black bars represent the median values. (b) BEAS-2B cells stably infected

with vector alone (φ) or vector expressing Nrf2 alleles were profiled for Nrf2 protein level by

immunoblot. (c) BEAS-2B cells from (b) were irradiated with either 223Ra or 137Cs. Data points

represent mean ± s.e. and are representative of at least three experiments. p-value <0.05 and q-

value (false discovery rate calculated using the two-stage step-up method) <0.01 were considered

statistically significant and denoted by an asterisk.

Figure 5. Gene expression changes, somatic copy number alterations (SCNA) and mutations

associated with cellular survival after α-particle irradiation. (a) Histogram and probability

density function of calculated integral survival values of 28 cell lines. (b) Single-sample gene set

enrichment analysis (ssGSEA) identifies gene sets that correlate with radiation sensitivity and

resistance. Heat map of ssGSEA scores (red = positive, blue = negative). Top gene sets,

organized by biological processes, are shown. (c) A subset of the top SCNA that correlate with




33

radiation resistance and sensitivity were organized by chromosomal positions. Red and blue bars

represent a SCNA in the corresponding gene that is associated with resistance and sensitivity,

respectively. (d) Top genes that when mutated were associated with resistance and sensitivity are

shown. Red and blue bars represent a mutation in the corresponding gene that is associated with

resistance and sensitivity, respectively. Heatmap of integral survival (red=resistant,

blue=sensitive) is parallel to the representations in (b), (c) and (d).

Figure 6. PIK3CA activation mutations confer decreased sensitivity to α-particles. (a) A map of

the PIK3CA mutations identified in the profiled cell lines displayed on the protein and its

domains. (b) BEAS-2B cells stably infected with vector alone (φ) or vector expressing PIK3CA

alleles were profiled for PI3K/AKT pathway activity by immunoblot. (c) The cytosol and nuclei

for each cell line were stained green and red, respectively. (d) Vector alone (φ) or vector

expressing PIK3CA alleles were profiled for survival from 223Ra irradiation. Data are expressed

as the area under the curve (AUC) and represent the mean ± s.e.m. of at least three independent

experiments. Vector alone (φ), vector expressing PIK3CA wild type or H1047R cells were

profiled using either the high content proliferation method (e) or by colony formation assay

(f&g). Data points represent mean ± s.e. and are representative of at least three experiments. p-

value <0.05 and q-value (false discovery rate calculated using the two-stage step-up method)

<0.01 were considered statistically significant and denoted by an asterisk. Representative

irradiated colonies in (g) are from a dose of 0.076 Gy of 223Ra.




0.01 0.1 1 100.0

0.5

1.0

1.5

223Ra (kBq/mL)

Prol

ifera

ting

Frac

tion

HCT15HMC18HSC4LC-1/SQSFLK2LUDLU-1MORCPR

NCIH1755OVKATERERFLCAISKMEL28BT474HEC1BMCF7

MDAMB453MHHNB11PC3SKNMCNCIH1563SQ1HCC202

LNCaPDMS53HCC15DMS114VCaPNCIH1666SW480

0

2

4

6

8

137Cs 223Ra0 2 4 60

2

4

6

AU

C (223 Ra

)

AUC(137Cs)

DMS114DMS53 HCC202

SW480

RERFLCAI

LC1SQSFH1666

Favor 223Ra

*

a.

b. c.

FIGURE 1

d.

AUC(CFA)

AU

C (HTP

)

R2= 0.55

*

AU

C

0.0 0.5 1.0 1.5-0.5

0.0

0.5

1.0

1.5

2.0

2.5




FIGURE2

a. HCC15 MHHNBll SKMEL28 SQl b. 15

I Cytoplasm I Nuclear

a5 10

MORCPR BT474 Hl563 Hl755

,.0

I 5

0 0 5 10 15 20 25

Radii (µm) LNCaP HSC4 LC-1/SQSF LK2 C.

223Ra ~ 219Rn

a/ 215Po --+ 211Pb

a 't LUDLU-1 DMS114 MCF7 HCC202 201Tl ~mBi

t= 10 days a

HCT15 HMC18 VCaP RERFLCAI ~"~ 47-71.5 µm

d. 3

R2= 0.97

DMS53 H1666 MDAMB453 PC3

i'.2 u ~ --<

SKNMC HEClB SW480 OVKATE

0 0.0 0.5 1.0 1.5

AUC[Gy]




0 1 2 30

1

2

3

+ Hydoxyapatite (kBq)

- Hyd

oxya

patit

e (k

Bq) R2=0.87

m =1.77

FIGURE 3

calciumoxygenphosphorous

cell

+ Hydoxyapatite (Gy)

- Hyd

oxya

patit

e (G

y) R2=0.87m =1.04

0.0 0.5 1.0 1.50.0

0.5

1.0

1.5a. b. d.

0.001 0.01 0.1 1 100.0

0.5

1.0

1.5

Ra223 (kBq/mL)

Prol

ifera

ting

Frac

tion

c.




FIGURE 4

NRF2

Actin

75 kDa

37 kDa

WT_1WT_2

T80K_1T80K_2

Nrf2 alellesa. b.

0

1

2

3

4

5

137Cs 223Ra

NFE2L2 del (16-34)KEAP1 E258*

0 2 4 6 80.01

0.1

1c.

Prol

ifera

ting

Frac

tion

Nrf2 wild type Nrf2 T80K

137Cs223Ra

**

KEAP1 G364C

AU

C [Gy]

Gray

**




ELVIDGE_HIF1A_AND_HIF2A_TARGETS_UP

VANTVEER_BREAST_CANCER_ESR1

ELVIDGE_HYPOXIA_DN

CHARAFE_BREAST_CANCER_LUMINAL_VS_BASALSMID_BREAST_CANCER_RELAPSE_IN_BONE_UP

ELVIDGE_HIF1A_AND_HIF2A_TARGETS

SMID_BREAST_CANCER_BASAL_DNSMID_BREAST_CANCER_ERBB2

DOANE_BREAST_CANCER_CLASSES_UPSMID_BREAST_CANCER_ERBB2_UP

SMID_BREAST_CANCER_LUMINAL_B_UPDOANE_BREAST_CANCER_CLASSES

YANG_BREAST_CANCER_ESR1_BULK

HAN_JNK_SINGALINGHALLMARK_PI3K_AKT_MTOR_SIGNALING

NIKOLSKY_BREAST_CANCER_19Q13.1_AMPLICON SMID_BREAST_CANCER_LUMINAL_B

REGULATION_OF_RHO_GTPASE_ACTIVITY

ELVIDGE_HYPOXIA_BY_DMOG_DN

SMID_BREAST_CANCER_RELAPSE_IN_LUNG_DN

REGULATION_OF_RAS_GTPASE_ACTIVITY

0.522

0.523

0.523

0.5240.53

0.536

0.5510.5550.5610.5630.5740.5790.583

0.5840.585

0.60.612

0.627

0.652

0.663

0.673

Survival d(dose)

IC FDRp-val0.15

0.15

0.000202 0.2830.000333 0.4

Hyp

oxia

Sign

alB

reas

t 0.000582 0.4230.000618 0.4230.000749 0.4310.00115 0.4610.00121 0.4610.00133 0.4670.00151 0.5070.00266 0.5390.00304 0.5390.00313 0.539

0.000166 0.280.000559 0.4230.000559 0.423

0.00238 0.5390.00314 0.5390.00311 0.539

0.15

SNORD124_AMPSOCS7_AMPERBB2_AMP

KAT7_AMP 0.4330.4580.463

0.592

DM

S114

HCC

202

DM

S53

BT47

4M

DAM

B453

VCA

PN

CIH

1563

SKM

EL28

MO

RCPR

NCI

H17

55H

SC4

LUD

LU1

HEC

1BLN

CAP

MCF

7H

CT15

PC3

LK2

NCI

H16

66H

MC1

8H

CC15

LC1S

QSF SQ

1SK

NM

CRE

RFLC

AI

OVK

ATE

SW48

0M

HH

NB1

1

0.000798 0.467

0.0097 0.6680.0104 0.6680.0153 0.668

17q12

17q21

MALT1_DELSMAD4_DEL

CDH2_DELSNORD58C_DEL

18q12-210.0425 10.0321 10.02 10.00731 1

MAP3K11 0.486PIK3CA 0.402

0.00958 0.9760.0382 0.976

HDAC9

SMG1 -0.32 0.0273 0.892ARID1B

-0.393 0.0043 0.892-0.339 0.0166 0.892

b.

c.

d.

FIGURE 5

Num

ber

0.0 0.4 0.8 1.20

2

4

6

8

10

AUC[Gy]

a.




a.

0

b.

¢

-200

PIK3CA alelles

PBK C2

400

-50 kDa

Actin

d.

1.4

1.3 ,-------t------=r J u l Ir ~ 1.1 , -----------==-~ 1.0 ·--·!·------!---~ --

f.

0.9, ---------

0.8....__ _______ _

•¢ ■ PIK3CAWT V PIK3CAH1047R

0.01 +---.----.---~ 0.00 0.05 0.10 0.15

Gray

e.

g.

E545K

H1047R

PIK --- r 600 800

I

I

0.01 --t----.--~-~ 0.0 0.5 1.0 1.5

Gray

Vector

1000 1068 aa

PIK3CAWT

PJK3CA Hl047R

• ¢ ■ PJK3CA wild type V PJK3CA Hl047R

¢

FIGURE6




Published OnlineFirst August 6, 2019.Cancer Res Brian D Yard, Priyanka Gopal, Kristina Bannik, et al. alpha-particle irradiationCellular and genetic determinants of the sensitivity of cancer to

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